11,172 research outputs found

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective

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    This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research. The proposed framework adeptly addresses common limitations of existing solutions, such as the neglect of data-driven estimation for vital production parameters, exclusive generation of point forecasts without considering model uncertainty, and lacking explanations regarding the sources of such uncertainty. Our approach employs Quantile Regression Forests for generating interval predictions, alongside both local and global variants of SHapley Additive Explanations for the examined predictive process monitoring problem. The practical applicability of the proposed methodology is substantiated through a real-world production planning case study, emphasizing the potential of prescriptive analytics in refining decision-making procedures. This paper accentuates the imperative of addressing these challenges to fully harness the extensive and rich data resources accessible for well-informed decision-making

    Hard magnetics in ultra-soft magnetorheological elastomers enhance fracture toughness and delay crack propagation

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    Pre-existing flaws in highly stretchable elastomers trigger fracture under large deformations. For multifunctional materials, fracture mechanics may be influenced by additional physical phenomena. This work studies the implications of hard magnetics on the fracture behaviour of ultra-soft magnetorheological elastomers (MREs). We experimentally demonstrate that MREs with remanent magnetisation have up to a 50 % higher fracture toughness than non pre-magnetised samples. Moreover, we report crack closure due to the magnetic field as a mechanism that delays the opening of cracks in pre-magnetised MREs. To overcome experimental limitations and provide further understanding, a phase-field model for the fracture of MREs is conceptualised. The numerical model incorporates magneto-mechanical coupling to demonstrate that the stress concentration at the crack tip is smaller when the MRE is pre-magnetised. Overall, this work unveils intriguing applications for functional actuators, with better fracture behaviour and potential better performance under cyclic loading

    Regionale Versicherungsrisiken unter dem morbiditÀtsorientierten Risikostrukturausgleich: Detektion, Ursachen und Reformbedarf der Wettbewerbsbedingungen in der GKV

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    Der Risikostrukturausgleich (RSA) ist der finanzielle Ausgleichsmechanismus zwischen den Krankenkassen. Er beschreibt, wie die Gelder des Gesundheitsfonds, dem Risiko gerecht, zwischen den Krankenkassen zu verteilen sind. Es ist das vordergrĂŒndige Ziel des RSA die Möglichkeit der Selektion von guten und schlechten Risiken (Risikoselektion) durch die Krankenkassen zu verhindern. Ohne einen RSA sind neben einem Verstoß gegen das SolidaritĂ€tsprinzip (BVerfG, Rn. 162 (18.07.2005)) Effizienzverluste durch die Verschiebung des Wettbewerbes zwischen den Krankenkassen von QualitĂ€t auf Risikoselektion (z.B. die Attrahierung von jungen und gesunden Personen), zu befĂŒrchten. Die These, die in dieser kumulativen Dissertation untersucht wird, ist, dass das Merkmal der regionalen Herkunft der Versicherten geeignet ist, um gute Risiken von schlechten Risiken zu trennen und somit Anreize zur Risikoselektion bietet. Es wird argumentiert, dass die rĂ€umliche Autokorrelation von individuellen DeckungsbeitrĂ€gen ein geeignetes Maß ist, um Anreize zur regionalen Risikoselektion zu erkennen. Dabei steht das Argument im Vordergrund, dass neben absoluten Deckungsbeitragsunterschieden die ValiditĂ€t der Information „regionale Herkunft“ fĂŒr Risikoselektion entscheidend ist. Die zweite Fragestellung der Dissertation betrifft die Ursachen der regionalen Risiken fĂŒr Krankenkassen. Die Identifikation von Ursachen verfolgt dabei das Ziel zu begrĂŒnden, ob die Versicherungsrisiken, die mit der regionalen Herkunft assoziiert sind, gemĂ€ĂŸ des SolidaritĂ€tsprinzips durch die Gesamtheit der Versichertengemeinschaft zu tragen wĂ€ren. Drittens wird die geographisch gewichtete Regression auf die Aspekte des Risikostrukturausgleichs angepasst und ein Verfahren beschrieben, wie die Regression auf dem sehr umfangreichen Datensatz des RSA effizient umgesetzt werden kann. Nach einer langen Debatte unter Gesundheitsökonomen wurde fĂŒr das Ausgleichsjahr 2021 erstmals eine Regionalisierung im RSA vorgenommen. Den Einzelveröffentlichungen dieser Dissertation war es beschieden, am gesundheitsökonomischen Diskurs teilzuhaben und letztlich die EinfĂŒhrung der Regionalisierung im RSA begleitet zu haben.:1 Einleitung 1.1 SolidaritĂ€t und Wettbewerb in der GKV 1.2 Motivation der Arbeit und Einordnung in die Literatur 1.3 Forschungsfragen und Gang der Arbeit 2 Der Einfluss der RegionalitĂ€t auf den Versicherungswettbewerb 2.1 Der wettbewerbliche Ordnungsrahmen der GKV 2.2 Dysfunktionale Folgen eines regional unvollstĂ€ndigen RSA 2.3 Maßzahlen der wettbewerblichen NeutralitĂ€t des 3 RĂ€umliche Versicherungsrisiken im solidarischen Wettbewerb 3.1 SolidaritĂ€t im RSA 3.2 Ursachen fĂŒr regionale Risiken 3.3 Einnahmerisiko 3.4 Mengen- und Strukturrisiko 3.5 Preisrisiko 4 Abbildung von rĂ€umlichen Versicherungsrisiken im RSA 4.1 Die Funktionsweise des RSA zwischen 2009 und 2020 4.2 Das M2-Modell 4.3 Das GWR-Modell 4.4 Ein empirischer Vergleich der RegionalisierungsansĂ€tze 5 Fazi

    Wavelet analysis and consensus algorithm-based fault-tolerant control for smart grids

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    In this paper, the voltage and frequency regulation problems are investigated for smart grids under the influence of faults. To solve those problem, a wavelet analysis and consensus algorithm-based fault-tolerant control scheme is proposed. Specifically, the wavelet analysis technique is introduced to determine whether there exist faults or not in the smart grids. Then, a distributed fault estimator is designed to estimate the attack signals. Based on this estimator state, a distributed fault-tolerant controller is designed to compensate for the faults. It is theoretically shown that the developed method can achieve the voltage regulation and frequency objectives. Finally, a smart grid with four distributed generations is constructed in MATLAB/Simulink for simulation to validate the effectiveness

    Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review

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    Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

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    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    Investigation of microparticle behavior in Newtonian, viscoelastic, and shear-thickening flows in straight microchannels

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    Sorting and separation of small substances such as cells, microorganisms, and micro- and nano-particles from a heterogeneous mixture is a common sample preparation step in many areas of biology, biotechnology, and medicine. Portability and inexpensive design of microfluidic-based sorting systems have benefited many of these biomedical applications. Accordingly, we have investigated microparticle hydrodynamics in fluids with various rheological behaviors (i.e., Newtonian, shear-thinning viscoelastic and shear-thickening non-Newtonian) flowing in straight microchannels. Numerical models were developed to simulate particles trajectories in Newtonian water and shear-thinning polyethylene oxide (PEO) solutions. The validated models were then used to perform numerical parametric studies and non-dimensional analysis on the Newtonian inertia-magnetic and shear-thinning elasto-inertal focusing regimes. Finally, the straight microfluidic device that was tested for Newtonian water and shear-thinning viscoelastic PEO solution, were adopted to experimentally study microparticle behavior in SiO2/Water shear-thickening nanofluid
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