738 research outputs found

    The study of renal function and toxicity using zebrafish (Danio rerio) larvae as a vertebrate model

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    Zebrafish (Danio rerio) is a powerful model in biomedical and pharmaceutical sciences. The zebrafish model was introduced to toxicological sciences in 1960, followed by its use in biomedical sciences to investigate vertebrate gene functions. As a consequence of many research projects in this field, the study of human genetic diseases became instantly feasible. Consequently, zebrafish have been intensively used in developmental biology and associated disciplines. Due to the simple administration of medicines and the high number of offspring, zebrafish larvae became widely more popular in pharmacological studies in the following years. In the past decade, zebrafish larvae were further established as a vertebrate model in the field of pharmacokinetics and nanomedicines. In this PhD thesis, zebrafish larvae were investigated as an earlystage in vivo vertebrate model to study renal function, toxicity, and were applied in drug-targeting projects using nanomedicines. The first part focused on the characterization of the renal function of three-to four-dayold zebrafish larvae. Non-renal elimination processes were additionally described. Moreover, injection techniques, imaging parameters, and post-image processing scripts were established to serve as a toolbox for follow-up projects. The second part analyzed the impact of gentamicin (a nephrotoxin) on the morphology of the pronephros of zebrafish larvae. Imaging methodologies such as fluorescent-based laser scanning microscopy and X-ray-based microtomography were applied. A profound comparison study of specimens acquired with different laboratory X-ray-based microtomography devices and a radiation facility was done to promote the use of X-ray-based microtomography for broader biomedical applications. In the third part, the toxicity of nephrotoxins on mitochondria in renal epithelial cells of proximal tubules was assessed using the zebrafish larva model. Findings were compared with other teleost models such as isolated renal tubules of killifish (Fundulus heteroclitus). In view of the usefulness and high predictability of the zebrafish model, it was applied to study the pharmacokinetics of novel nanoparticles in the fourth part. Various in vivo pharmacokinetic parameters such as drug release, transfection of mRNA/pDNA plasmids, macrophage clearance, and the characterization of novel drug carriers that were manipulated with ultrasound were assessed in multiple collaborative projects. Altogether, the presented zebrafish model showed to be a reliable in vivo vertebrate model to assess renal function, toxicity, and pharmacokinetics of nanoparticles. The application of the presented model will hopefully encourage others to reduce animal experiments in preliminary studies by fostering the use of zebrafish larvae

    Inkjet printing digital image generation and compensation for surface chemistry effects

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    Additive manufacturing (AM) of electronic materials using digital inkjet printing (DIJP) is of research interests nowadays because of its potential benefits in the semiconductor industry. Current trends in manufacturing electronics feature DIJP as a key technology to enable the production of customised and microscale functional devices. However, the fabrication of microelectronic components at large scale demands fast printing of tight features with high dimensional accuracy on substrates with varied surface topography which push inkjet printing process to its limits. To understand the DIJP droplet deposition on such substrates, generally requires computational fluid dynamics modelling which is limited in its physics approximation of surface interactions. Otherwise, a kind of “trial and error” approach to determining how the ink spreads, coalesce and solidifies over the substrate is used, often a very time-consuming process. Consequently, this thesis aims to develop new modelling techniques to predict fast and accurately the surface morphology of inkjet-printed features, enabling the optimisation of DIJP control parameters and the compensation of images for better dimensional accuracy of printed electronics devices. This investigation explored three categories of modelling techniques to predict the surface morphology of inkjet-printed features: physics-based, data-driven and hybrid physics-based and data-driven. Two physics-based numerical models were developed to reproduce the inkjet printing droplet deposition and solidification processes using a lattice Boltzmann (LB) multiphase flow model and a finite element (FE) chemo-mechanical model, respectively. The LB model was limited to the simulation of single tracks and small square films and the FE model was mainly employed for the distortion prediction of multilayer objects. Alternatively, two data-driven models were implemented to reconstruct the surface morphology of single tracks and free-form films using images from experiments: image analysis (IA) and shape from shading (SFS). IA assumed volume conservation and minimal energy drop shape to reconstruct the surface while SFS resolved the height of the image using a reflection model. Finally, a hybrid physics-based and data-driven approach was generated which incorporates the uncertainty of droplet landing position and footprint, hydrostatic analytical models, empirical correlations derived from experiments, and relationships derived from physics-based models to predict fast and accurately any free-form layer pattern as a function of physical properties, printing parameters and wetting characteristics. Depending on the selection of the modelling technique to predict the deformed geometry, further considerations were required. For the purely physics-based and data-driven models, a surrogate model using response surface equations was employed to create a transfer function between printing parameters, substrate wetting characteristics and the resulting surface morphology. The development of a transfer function significantly decreased the computational time required by purely physics-based models and enabled the parameter optimisation using a multi-objective genetic algorithm approach to attain the best film dimensional accuracy. Additionally, for multilayer printing applications, a layer compensation approach was achieved utilizing a convolutional neural network trained by the predicted (deformed) geometry to reduce the out of plane error to target shape. The optimal combination of printing parameters and input image compensation helped with the generation of fine features that are traditionally difficult for inkjet, improved resolution of edges and corners by reducing the amount of overflow from material, accounted for varied topography and capillary effects thereof on the substrate surface and considered the effect of multiple layers built up on each other. This study revealed for the first time to the best of our knowledge the role of the droplet location and footprint diameter uncertainty in the stability and uniformity of printed features. Using a droplet overlap map which was proposed as a universal technique to assess the effect of printing parameters on pattern geometry, it was shown that reliable limits for break-up and bulging of printed features were obtained. Considering droplet position and diameter size uncertainties, predicted optimal printing parameters improved the quality of printed films on substrates with different wettability. Finally, a stability diagram illustrating the onset of bulging and separation for lines and films as well as the optimal drop spacing, printing frequency and stand-off distance was generated to inform visually the results. This investigation has developed a predictive physics-based model of the surface morphology of DIJP features on heterogeneous substrates and a methodology to find the printing parameters and compensate the layer geometry required for optimum part dimensional accuracy. The simplicity of the proposed technique makes it a promising tool for model driven inkjet printing process optimization, including real time process control and paves the way for better quality devices in the printed electronics industry

    Analysis, Design and Fabrication of Micromixers, Volume II

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    Micromixers are an important component in micrototal analysis systems and lab-on-a-chip platforms which are widely used for sample preparation and analysis, drug delivery, and biological and chemical synthesis. The Special Issue "Analysis, Design and Fabrication of Micromixers II" published in Micromachines covers new mechanisms, numerical and/or experimental mixing analysis, design, and fabrication of various micromixers. This reprint includes an editorial, two review papers, and eleven research papers reporting on five active and six passive micromixers. Three of the active micromixers have electrokinetic driving force, but the other two are activated by mechanical mechanism and acoustic streaming. Three studies employs non-Newtonian working fluids, one of which deals with nano-non-Newtonian fluids. Most of the cases investigated micromixer design

    Edoardo Benvenuto Prize. Collection of papers

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    The promotion of studies and research on the science and art of building in their historical development constitutes the objective that the Edoardo Benvenuto Association has set itself, since its establishment, in order to honor the memory of Edoardo Benvenuto (1940-1998). The Association in recent years has achieved interesting results by developing various activities such as: organization of national and international meetings, conferences, study days; collaborations with national and foreign research institutions; promotion of the editorial series “Between Mechanics and Architecture"; activation of the portal Bibliotheca Mechanica Architectonica, first “open source” digitized library dedicated to historical research on mechanical and architectural texts. But perhaps the most qualifying initiative was the institution of the Edoardo Benvenuto Prize, arrived in 2019 in its twelfth edition, reserved for young researchers in the field of historical studies on science and the art of building. The awarding of the Prize takes place after an in-depth examination of the texts received by the Association by an international commission of experts. The purpose of this book is to collect and present the most recent studies and publications produced by the winners of the various editions of the Edoardo Benvenuto Prize

    Autonomous Sensing Nodes for IoT Applications

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    The present doctoral thesis fits into the energy harvesting framework, presenting the development of low-power nodes compliant with the energy autonomy requirement, and sharing common technologies and architectures, but based on different energy sources and sensing mechanisms. The adopted approach is aimed at evaluating multiple aspects of the system in its entirety (i.e., the energy harvesting mechanism, the choice of the harvester, the study of the sensing process, the selection of the electronic devices for processing, acquisition and measurement, the electronic design, the microcontroller unit (MCU) programming techniques), accounting for very challenging constraints as the low amounts of harvested power (i.e., [μW, mW] range), the careful management of the available energy, the coexistence of sensing and radio transmitting features with ultra-low power requirements. Commercial sensors are mainly used to meet the cost-effectiveness and the large-scale reproducibility requirements, however also customized sensors for a specific application (soil moisture measurement), together with appropriate characterization and reading circuits, are also presented. Two different strategies have been pursued which led to the development of two types of sensor nodes, which are referred to as 'sensor tags' and 'self-sufficient sensor nodes'. The first term refers to completely passive sensor nodes without an on-board battery as storage element and which operate only in the presence of the energy source, provisioning energy from it. In this thesis, an RFID (Radio Frequency Identification) sensor tag for soil moisture monitoring powered by the impinging electromagnetic field is presented. The second term identifies sensor nodes equipped with a battery rechargeable through energy scavenging and working as a secondary reserve in case of absence of the primary energy source. In this thesis, quasi-real-time multi-purpose monitoring LoRaWAN nodes harvesting energy from thermoelectricity, diffused solar light, indoor white light, and artificial colored light are presented

    Exploring Robot Teleoperation in Virtual Reality

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    This thesis presents research on VR-based robot teleoperation with a focus on remote environment visualisation in virtual reality, the effects of remote environment reconstruction scale in virtual reality on the human-operator's ability to control the robot and human-operator's visual attention patterns when teleoperating a robot from virtual reality. A VR-based robot teleoperation framework was developed, it is compatible with various robotic systems and cameras, allowing for teleoperation and supervised control with any ROS-compatible robot and visualisation of the environment through any ROS-compatible RGB and RGBD cameras. The framework includes mapping, segmentation, tactile exploration, and non-physically demanding VR interface navigation and controls through any Unity-compatible VR headset and controllers or haptic devices. Point clouds are a common way to visualise remote environments in 3D, but they often have distortions and occlusions, making it difficult to accurately represent objects' textures. This can lead to poor decision-making during teleoperation if objects are inaccurately represented in the VR reconstruction. A study using an end-effector-mounted RGBD camera with OctoMap mapping of the remote environment was conducted to explore the remote environment with fewer point cloud distortions and occlusions while using a relatively small bandwidth. Additionally, a tactile exploration study proposed a novel method for visually presenting information about objects' materials in the VR interface, to improve the operator's decision-making and address the challenges of point cloud visualisation. Two studies have been conducted to understand the effect of virtual world dynamic scaling on teleoperation flow. The first study investigated the use of rate mode control with constant and variable mapping of the operator's joystick position to the speed (rate) of the robot's end-effector, depending on the virtual world scale. The results showed that variable mapping allowed participants to teleoperate the robot more effectively but at the cost of increased perceived workload. The second study compared how operators used a virtual world scale in supervised control, comparing the virtual world scale of participants at the beginning and end of a 3-day experiment. The results showed that as operators got better at the task they as a group used a different virtual world scale, and participants' prior video gaming experience also affected the virtual world scale chosen by operators. Similarly, the human-operator's visual attention study has investigated how their visual attention changes as they become better at teleoperating a robot using the framework. The results revealed the most important objects in the VR reconstructed remote environment as indicated by operators' visual attention patterns as well as their visual priorities shifts as they got better at teleoperating the robot. The study also demonstrated that operators’ prior video gaming experience affects their ability to teleoperate the robot and their visual attention behaviours

    Zooming in on the Universe: In Search of Quantum Spacetime

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    This thesis investigates low-dimensional models of nonperturbative quantum gravity, with a special focus on Causal Dynamical Triangulations (CDT). We define the so-called curvature profile, a new quantum gravitational observable based on the quantum Ricci curvature. We subsequently study its coarse-graining capabilities on a class of regular, two-dimensional polygons with isolated curvature singularities, and we determine the curvature profile of (1+1)-dimensional CDT with toroidal topology. Next, we focus on CDT in 2+1 dimensions, intvestigating the behavior of the two-dimensional spatial slice geometries. We then turn our attention to matrix models, exploring a differential reformulation of the integrals over one- and two-matrix ensembles. Finally, we provide a hands-on introduction to computer simulations of CDT quantum gravity.Comment: Ph.D. thesi

    Coherent and Holographic Imaging Methods for Immersive Near-Eye Displays

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    Lähinäytöt on suunniteltu tarjoamaan realistisia kolmiulotteisia katselukokemuksia, joille on merkittävää tarvetta esimerkiksi työkoneiden etäkäytössä ja 3D-suunnittelussa. Nykyaikaiset lähinäytöt tuottavat kuitenkin edelleen ristiriitaisia visuaalisia vihjeitä, jotka heikentävät immersiivistä kokemusta ja haittaavat niiden miellyttävää käyttöä. Merkittävänä ratkaisuvaihtoehtona pidetään koherentin valon, kuten laservalon, käyttöä näytön valaistukseen, millä voidaan korjata nykyisten lähinäyttöjen puutteita. Erityisesti koherentti valaistus mahdollistaa holografisen kuvantamisen, jota käyttävät holografiset näytöt voivat tarkasti jäljitellä kolmiulotteisten mallien todellisia valoaaltoja. Koherentin valon käyttäminen näyttöjen valaisemiseen aiheuttaa kuitenkin huomiota vaativaa korkean kontrastin häiriötä pilkkukuvioiden muodossa. Lisäksi holografisten näyttöjen laskentamenetelmät ovat laskennallisesti vaativia ja asettavat uusia haasteita analyysin, pilkkuhäiriön ja valon mallintamisen suhteen. Tässä väitöskirjassa tutkitaan laskennallisia menetelmiä lähinäytöille koherentissa kuvantamisjärjestelmässä käyttäen signaalinkäsittelyä, koneoppimista sekä geometrista (säde) ja fysikaalista (aalto) optiikan mallintamista. Työn ensimmäisessä osassa keskitytään holografisten kuvantamismuotojen analysointiin sekä kehitetään hologrammien laskennallisia menetelmiä. Holografian korkeiden laskentavaatimusten ratkaisemiseksi otamme käyttöön holografiset stereogrammit holografisen datan likimääräisenä esitysmuotona. Tarkastelemme kyseisen esitysmuodon visuaalista oikeellisuutta kehittämällä analyysikehyksen holografisen stereogrammin tarjoamien visuaalisten vihjeiden tarkkuudelle akkommodaatiota varten suhteessa sen suunnitteluparametreihin. Lisäksi ehdotamme signaalinkäsittelyratkaisua pilkkuhäiriön vähentämiseksi, ratkaistaksemme nykyisten menetelmien valon mallintamiseen liittyvät visuaalisia artefakteja aiheuttavat ongelmat. Kehitämme myös uudenlaisen holografisen kuvantamismenetelmän, jolla voidaan mallintaa tarkasti valon käyttäytymistä haastavissa olosuhteissa, kuten peiliheijastuksissa. Väitöskirjan toisessa osassa lähestytään koherentin näyttökuvantamisen laskennallista taakkaa koneoppimisen avulla. Kehitämme koherentin akkommodaatioinvariantin lähinäytön suunnittelukehyksen, jossa optimoidaan yhtäaikaisesti näytön staattista optiikka ja näytön kuvan esikäsittelyverkkoa. Lopuksi nopeutamme ehdottamaamme uutta holografista kuvantamismenetelmää koneoppimisen avulla reaaliaikaisia sovelluksia varten. Kyseiseen ratkaisuun sisältyy myös tehokkaan menettelyn kehittäminen funktionaalisten satunnais-3D-ympäristöjen tuottamiseksi. Kehittämämme menetelmä mahdollistaa suurten synteettisten moninäkökulmaisten kuvien datasettien tuottamisen, joilla voidaan kouluttaa sopivia neuroverkkoja mallintamaan holografista kuvantamismenetelmäämme reaaliajassa. Kaiken kaikkiaan tässä työssä kehitettyjen menetelmien osoitetaan olevan erittäin kilpailukykyisiä uusimpien koherentin valon lähinäyttöjen laskentamenetelmien kanssa. Työn tuloksena nähdään kaksi vaihtoehtoista lähestymistapaa ristiriitaisten visuaalisten vihjeiden aiheuttamien nykyisten lähinäyttöongelmien ratkaisemiseksi joko staattisella tai dynaamisella optiikalla ja reaaliaikaiseen käyttöön soveltuvilla laskentamenetelmillä. Esitetyt tulokset ovat näin ollen tärkeitä seuraavan sukupolven immersiivisille lähinäytöille.Near-eye displays have been designed to provide realistic 3D viewing experience, strongly demanded in applications, such as remote machine operation, entertainment, and 3D design. However, contemporary near-eye displays still generate conflicting visual cues which degrade the immersive experience and hinders their comfortable use. Approaches using coherent, e.g., laser light for display illumination have been considered prominent for tackling the current near-eye display deficiencies. Coherent illumination enables holographic imaging whereas holographic displays are expected to accurately recreate the true light waves of a desired 3D scene. However, the use of coherent light for driving displays introduces additional high contrast noise in the form of speckle patterns, which has to be taken care of. Furthermore, imaging methods for holographic displays are computationally demanding and impose new challenges in analysis, speckle noise and light modelling. This thesis examines computational methods for near-eye displays in the coherent imaging regime using signal processing, machine learning, and geometrical (ray) and physical (wave) optics modeling. In the first part of the thesis, we concentrate on analysis of holographic imaging modalities and develop corresponding computational methods. To tackle the high computational demands of holography, we adopt holographic stereograms as an approximative holographic data representation. We address the visual correctness of such representation by developing a framework for analyzing the accuracy of accommodation visual cues provided by a holographic stereogram in relation to its design parameters. Additionally, we propose a signal processing solution for speckle noise reduction to overcome existing issues in light modelling causing visual artefacts. We also develop a novel holographic imaging method to accurately model lighting effects in challenging conditions, such as mirror reflections. In the second part of the thesis, we approach the computational complexity aspects of coherent display imaging through deep learning. We develop a coherent accommodation-invariant near-eye display framework to jointly optimize static display optics and a display image pre-processing network. Finally, we accelerate the corresponding novel holographic imaging method via deep learning aimed at real-time applications. This includes developing an efficient procedure for generating functional random 3D scenes for forming a large synthetic data set of multiperspective images, and training a neural network to approximate the holographic imaging method under the real-time processing constraints. Altogether, the methods developed in this thesis are shown to be highly competitive with the state-of-the-art computational methods for coherent-light near-eye displays. The results of the work demonstrate two alternative approaches for resolving the existing near-eye display problems of conflicting visual cues using either static or dynamic optics and computational methods suitable for real-time use. The presented results are therefore instrumental for the next-generation immersive near-eye displays
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