988 research outputs found

    Assessing Big Data Analytics Performance in Industry 5.0 Operations: A Comparative Experiment

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    Big Data analytics performance is critical in the dynamic world of Industry 5.0, where human engagement with cutting-edge technology is essential. Based on a comparison experiment, this empirical research highlights the significance of optimal data processing algorithms by providing important insights into the relationship between data amount and processing speed. The requirement of resource-intensive demands for efficient resource allocation and optimization in Industry 5.0 operations is emphasized. Operation C's exceptional performance in terms of mistake rates, data correctness, and processing quality highlights the need of careful data management procedures. As Industry 5.0 develops, scalability becomes more important. Operation C is a perfect example of how to adapt to higher data volumes. The way forward for an industrial future that is more responsive, sustainable, and efficient is shaped by this study

    Development of a SQUID magnetometry system for cryogenic neutron electric dipole moment experiment

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    A measurement of the neutron electric dipole moment (nEDM) could hold the key to understanding why the visible universe is the way it is: why matter should predominate over antimatter. As a charge-parity violating (CPV) quantity, an nEDM could provide an insight into new mechanisms that address this baryon asymmetry. The motivation for an improved sensitivity to an nEDM is to find it to be non-zero at a level consistent with certain beyond the Standard Model theories that predict new sources of CPV, or to establish a new limit that constrains them. CryoEDM is an experiment that sought to better the current limit of ∣dn∣<2.9×10−26 e |d_n| < 2.9 \times 10^{-26}\,e\,cm by an order of magnitude. It is designed to measure the nEDM via the Ramsey Method of Separated Oscillatory Fields, in which it is critical that the magnetic field remains stable throughout. A way of accurately tracking the magnetic fields, moreover at a temperature ∼0.5 \sim 0.5\,K, is crucial for CryoEDM, and for future cryogenic projects. This thesis presents work focussing on the development of a 12-SQUID magnetometry system for CryoEDM, that enables the magnetic field to be monitored to a precision of 0.1 0.1\,pT. A major component of its infrastructure is the superconducting capillary shields, which screen the input lines of the SQUIDs from the pick up of spurious magnetic fields that will perturb a SQUID's measurement. These are shown to have a transverse shielding factor of >1×107> 1 \times 10^{7}, which is a few orders of magnitude greater than the calculated requirement. Efforts to characterise the shielding of the SQUID chips themselves are also discussed. The use of Cryoperm for shields reveals a tension between improved SQUID noise and worse neutron statistics. Investigations show that without it, SQUIDs have an elevated noise when cooled in a substantial magnetic field; with it, magnetostatic simulations suggest that it is detrimental to the polarisation of neutrons in transport. The findings suggest that with proper consideration, it is possible to reach a compromise between the two behaviours. Computational work to develop a simulation of SQUID data is detailed, which is based on the Laplace equation for the magnetic scalar potential. These data are ultimately used in the development of a linear regression technique to determine the volume-averaged magnetic field in the neutron cells. This proves highly effective in determining the fields within the 0.1 0.1\,pT requirement under certain conditions

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    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

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    On factor models for high-dimensional time series

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    The aim of this thesis is to develop statistical methods for use with factor models for high-dimensional time series. We consider three broad areas: estimation, changepoint detection, and determination of the number of factors. In Chapter 1, we sketch the backdrop for our thesis and review key aspects of the literature. In Chapter 2, we develop a method to estimate the factors and parameters in an approximate dynamic factor model. Specifically, we present a spectral expectation-maximisation (or \spectral EM") algorithm, whereby we derive the E and M step equations in the frequency domain. Our E step relies on the Wiener-Kolmogorov smoother, the frequency domain counterpart of the Kalman smoother, and our M step is based on maximisation of the Whittle Likelihood with respect to the parameters of the model. We initialise our procedure using dynamic principal components analysis (or \dynamic PCA"), and by leveraging results on lag-window estimators of spectral density by Wu and Zaffaroni (2018), we establish consistency-with-rates of our spectral EM estimator of the parameters and factors as both the dimension (N) and the sample size (T) go to infinity. We find rates commensurate with the literature. Finally, we conduct a simulation study to numerically validate our theoretical results. In Chapter 3, we develop a sequential procedure to detect changepoints in an approximate static factor model. Specifically, we define a ratio of eigenvalues of the covariance matrix of N observed variables. We compute this ratio each period using a rolling window of size m over time, and declare a changepoint when its value breaches an alarm threshold. We investigate the asymptotic behaviour (as N;m ! 1) of our ratio, and prove that, for specific eigenvalues, the ratio will spike upwards when a changepoint is encountered but not otherwise. We use a block-bootstrap to obtain alarm thresholds. We present simulation results and an empirical application based on Financial Times Stock Exchange 100 Index (or \FTSE 100") data. In Chapter 4, we conduct an exploratory analysis which aims to extend the randomised sequential procedure of Trapani (2018) into the frequency domain. Specifically, we aim to estimate the number of dynamically loaded factors by applying the test of Trapani (2018) to eigenvalues of the estimated spectral density matrix (as opposed to the covariance matrix) of the data

    Visual place recognition for improved open and uncertain navigation

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    Visual place recognition localises a query place image by comparing it against a reference database of known place images, a fundamental element of robotic navigation. Recent work focuses on using deep learning to learn image descriptors for this task that are invariant to appearance changes from dynamic lighting, weather and seasonal conditions. However, these descriptors: require greater computational resources than are available on robotic hardware, have few SLAM frameworks designed to utilise them, return a relative comparison between image descriptors which is difficult to interpret, cannot be used for appearance invariance in other navigation tasks such as scene classification and are unable to identify query images from an open environment that have no true match in the reference database. This thesis addresses these challenges with three contributions. The first is a lightweight visual place recognition descriptor combined with a probabilistic filter to address a subset of the visual SLAM problem in real-time. The second contribution combines visual place recognition and scene classification for appearance invariant scene classification, which is extended to recognise unknown scene classes when navigating an open environment. The final contribution uses comparisons between query and reference image descriptors to classify whether they result in a true, or false positive localisation and whether a true match for the query image exists in the reference database.Edinburgh Centre for Robotics and Engineering and Physical Sciences Research Council (EPSRC) fundin

    The Socio-Technical Dynamics of Renewable Energy Policies in Germany

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    Growing environmental concerns and human-caused climate change increase the pressure on policymakers for rapid action to transform how societies convert energy, produce goods, or transport freight. Innovation and technological progress may contribute to such transitions. However, technological change is hard to predict, requires time, and may be laden with political conflicts. Although more sustainable technologies are available, incentivizing demand and deployment are crucial to accelerate transitions. As transformations develop over decades, understanding the temporal dynamics of policies is critical for governance. In Germany, the renewable energy act incentivizes the deployment of renewable energy technologies by remunerating electricity fed into the common grid. This dissertation assesses how socio-technical developments of solar and wind energy conversion technologies and the renewable energy act interactively shaped each other. Drawing on frameworks such as technological innovation systems, legitimacy, framing, and policy feedback, the contents of 16,485 newspaper articles and additional empirical studies were scrutinized. Combining methods from natural language processing, machine learning, and statistics, this thesis develops text models to assess changes in content and sentiment in large corpora over time. Three studies focus on the shifts in media framing of the German renewable energy act, the underlying co-evolution of technological and policy processes, and the development of the legitimacy of wind power. The results confirm that renewable energy deployment and policy are contested with varying intensity over time. Where change ought to occur, non-linear dynamics of innovation and technology uptake, growing policy costs, economic interests of incumbents, and technology side effects increasingly complicate policymaking over time. The early phases of the renewable energy act were shaped by positive expectations toward renewable energy technologies, which later shifted towards an emphasis on policy costs. The findings highlight the importance of the prosperity of underlying innovation systems as supporters of policy ambition and maintenance over time. However, policy costs and side effects must be managed effectively to withstand increasing contestation. These results may contribute to advancing the successful governance of sectoral transitions likely to unfold over several decades
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