4,987 research outputs found

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    Dr. KID: Direct Remeshing and K-set Isometric Decomposition for Scalable Physicalization of Organic Shapes

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    Dr. KID is an algorithm that uses isometric decomposition for the physicalization of potato-shaped organic models in a puzzle fashion. The algorithm begins with creating a simple, regular triangular surface mesh of organic shapes, followed by iterative k-means clustering and remeshing. For clustering, we need similarity between triangles (segments) which is defined as a distance function. The distance function maps each triangle's shape to a single point in the virtual 3D space. Thus, the distance between the triangles indicates their degree of dissimilarity. K-means clustering uses this distance and sorts of segments into k classes. After this, remeshing is applied to minimize the distance between triangles within the same cluster by making their shapes identical. Clustering and remeshing are repeated until the distance between triangles in the same cluster reaches an acceptable threshold. We adopt a curvature-aware strategy to determine the surface thickness and finalize puzzle pieces for 3D printing. Identical hinges and holes are created for assembling the puzzle components. For smoother outcomes, we use triangle subdivision along with curvature-aware clustering, generating curved triangular patches for 3D printing. Our algorithm was evaluated using various models, and the 3D-printed results were analyzed. Findings indicate that our algorithm performs reliably on target organic shapes with minimal loss of input geometry

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    The future of cosmology? A case for CMB spectral distortions

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    This thesis treats the topic of CMB Spectral Distortions (SDs), which represent any deviation from a pure black body shape of the CMB energy spectrum. As such, they can be used to probe the inflationary, expansion and thermal evolution of the universe both within Λ\LambdaCDM and beyond it. The currently missing observation of this rich probe of the universe makes of it an ideal target for future observational campaigns. In fact, while the Λ\LambdaCDM signal guarantees a discovery, the sensitivity to a wide variety of new physics opens the door to an enormous uncharted territory. In light of these considerations, the thesis opens by reviewing the topic of CMB SDs in a pedagogical and illustrative fashion, aimed at waking the interest of the broader community. This introductory premise sets the stage for the first main contribution of the thesis to the field of SDs: their implementation in the Boltzmann solver CLASS and the parameter inference code MontePython. The CLASS+MontePython pipeline is publicly available, fast, it includes all sources of SDs within Λ\LambdaCDM and many others beyond that, and allows to consistently account for any observational setup. By means of these numerical tools, the second main contribution of the thesis consists in showcasing the versatility and competitiveness of SDs for several cosmological models as well as for a number of different mission designs. Among others, the results cover features in the primordial power spectrum, primordial gravitational waves, non-standard dark matter properties, primordial black holes, primordial magnetic fields and Hubble tension. Finally, the manuscript is disseminated with (20) follow-up ideas that naturally extend the work carried out so far, highlighting how rich of unexplored possibilities the field of CMB SDs still is. The hope is that these suggestions will become a propeller for further interesting developments.Comment: PhD thesis. Pedagogical review of theory, experimental status and numerical tools (CLASS+MontePython) with broad overview of applications. Includes 20 original follow-up idea

    Optimising water quality outcomes for complex water resource systems and water grids

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    As the world progresses, water resources are likely to be subjected to much greater pressures than in the past. Even though the principal water problem revolves around inadequate and uncertain water supplies, water quality management plays an equally important role. Availability of good quality water is paramount to sustainability of human population as well as the environment. Achieving water quality and quantity objectives can be conflicting and becomes more complicated with challenges like, climate change, growing populations and changed land uses. Managing adequate water quality in a reservoir gets complicated by multiple inflows with different water quality levels often resulting in poor water quality. Hence, it is fundamental to approach this issue in a more systematic, comprehensive, and coordinated fashion. Most previous studies related to water resources management focused on water quantity and considered water quality separately. However, this research study focused on considering water quantity and quality objectives simultaneously in a single model to explore and understand the relationship between them in a reservoir system. A case study area was identified in Western Victoria, Australia with water quantity and quality challenges. Taylors Lake of Grampians System in Victoria, Australia receives water from multiple sources of differing quality and quantity and has the abovesaid problems. A combined simulation and optimisation approach was adopted to carry out the analysis. A multi-objective optimisation approach was applied to achieve optimal water availability and quality in the storage. The multi-objective optimisation model included three objective functions which were: water volume and two water quality parameters: salinity and turbidity. Results showed competing nature of water quantity and quality objectives and established the trade-offs. It further showed that it was possible to generate a range of optimal solutions to effectively manage those trade-offs. The trade-off analysis explored and informed that selective harvesting of inflows is effective to improve water quality in storage. However, with strict water quality restriction there is a considerable loss in water volume. The robustness of the optimisation approach used in this study was confirmed through sensitivity and uncertainty analysis. The research work also incorporated various spatio-temporal scenario analyses to systematically articulate long-term and short-term operational planning strategies. Operational decisions around possible harvesting regimes while achieving optimal water quantity and quality and meeting all water demands were established. The climate change analysis revealed that optimal management of water quantity and quality in storage became extremely challenging under future climate projections. The high reduction in storage volume in the future will lead to several challenges such as water supply shortfall and inability to undertake selective harvesting due to reduced water quality levels. In this context, selective harvesting of inflows based on water quality will no longer be an option to manage water quantity and quality optimally in storage. Some significant conclusions of this research work included the establishment of trade-offs between water quality and quantity objectives particular to this configuration of water supply system. The work demonstrated that selective harvesting of inflows will improve the stored water quality, and this finding along with the approach used is a significant contribution to decision makers working within the water sector. The simulation-optimisation approach is very effective in providing a range of optimal solutions, which can be used to make more informed decisions around achieving optimal water quality and quantity in storage. It was further demonstrated that there are range of planning periods, both long-term (>10 years) and short-term (<1 year), all of which offer distinct advantages and provides useful insights, making this an additional key contribution of the work. Importantly, climate change was also considered where it was found that diminishing water resources, particularly to this geographic location, makes it increasingly difficult to optimise both quality and quantity in storage providing further useful insights from this work.Doctor of Philosoph

    Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

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    In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

    Land Use and Land Cover Mapping in a Changing World

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    It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classification systems

    ATR-FTIR Spectroscopy-Linked Chemometrics:A Novel Approach to the Analysis and Control of the Invasive Species Japanese Knotweed

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    Japanese knotweed (Reynoutria japonica), an invasive plant species, causes negative environmental and socio-economic impacts. A female clone in the United Kingdom, its extensive rhizome system enables rapid vegetative spread. Plasticity permits this species to occupy a broad geographic range and survive harsh abiotic conditions. It is notoriously difficult to control with traditional management strategies, which include repetitive herbicide application and costly carbon-intensive rhizome excavation. This problem is complicated by crossbreeding with the closely related species, Giant knotweed (Reynoutria sachalinensis), to give the more vigorous hybrid, Bohemian knotweed (Fallopia x Bohemica) which produces viable seed. These species, hybrids, and backcrosses form a morphologically similar complex known as Japanese knotweed ‘sensu lato’ and are often misidentified. The research herein explores the opportunities offered by advances in the application of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy-linked chemometrics within plant sciences, for the identification and control of knotweed, to enhance our understanding of knotweed biology, and the potential of this technique. ATR-FTIR spectral profiles of Japanese knotweed leaf material and xylem sap samples, which include important biological absorptions due to lipids, proteins, carbohydrates, and nucleic acids, were used to: identify plants from different growing regions highlighting the plasticity of this clonal species; differentiate between related species and hybrids; and predict key physiological characteristics such as hormone concentrations and root water potential. Technical advances were made for the application of ATR-FTIR spectroscopy to plant science, including definition of the environmental factors that exert the most significant influence on spectral profiles, evaluation of sample preparation techniques, and identification of key wavenumbers for prediction of hormone concentrations and abiotic stress. The presented results cement the position of concatenated mid-infrared spectroscopy and machine learning as a powerful approach for the study of plant biology, extending its reach beyond the field of crop science to demonstrate a potential for the discrimination between and control of invasive plant species
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