5,773 research outputs found

    Challenges in the Design and Implementation of IoT Testbeds in Smart-Cities : A Systematic Review

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    Advancements in wireless communication and the increased accessibility to low-cost sensing and data processing IoT technologies have increased the research and development of urban monitoring systems. Most smart city research projects rely on deploying proprietary IoT testbeds for indoor and outdoor data collection. Such testbeds typically rely on a three-tier architecture composed of the Endpoint, the Edge, and the Cloud. Managing the system's operation whilst considering the security and privacy challenges that emerge, such as data privacy controls, network security, and security updates on the devices, is challenging. This work presents a systematic study of the challenges of developing, deploying and managing urban monitoring testbeds, as experienced in a series of urban monitoring research projects, followed by an analysis of the relevant literature. By identifying the challenges in the various projects and organising them under the V-model development lifecycle levels, we provide a reference guide for future projects. Understanding the challenges early on will facilitate current and future smart-cities IoT research projects to reduce implementation time and deliver secure and resilient testbeds

    Double Copy from Tensor Products of Metric BV{}^{\color{gray} \blacksquare}-algebras

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    Field theories with kinematic Lie algebras, such as field theories featuring colour-kinematics duality, possess an underlying algebraic structure known as BV{}^{\color{gray} \blacksquare}-algebra. If, additionally, matter fields are present, this structure is supplemented by a module for the BV{}^{\color{gray} \blacksquare}-algebra. We explain this perspective, expanding on our previous work and providing many additional mathematical details. We also show how the tensor product of two metric BV{}^{\color{gray} \blacksquare}-algebras yields the action of a new syngamy field theory, a construction which comprises the familiar double copy construction. As examples, we discuss various scalar field theories, Chern-Simons theory, self-dual Yang-Mills theory, and the pure spinor formulations of both M2-brane models and supersymmetric Yang-Mills theory. The latter leads to a new cubic pure spinor action for ten-dimensional supergravity. We also give a homotopy-algebraic perspective on colour-flavour-stripping, obtain a new restricted tensor product over a wide class of bialgebras, and we show that any field theory (even one without colour-kinematics duality) comes with a kinematic LL_\infty-algebra.Comment: v2: 97 pages, references added, typos fixed, comments welcom

    The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: a critical review

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    With the predicted depletion of natural resources and alarming environmental issues, sustainable development has become a popular as well as a much-needed concept in modern process industries. Hence, manufacturers are quite keen on adopting novel process monitoring techniques to enhance product quality and process efficiency while minimizing possible adverse environmental impacts. Hardware sensors are employed in process industries to aid process monitoring and control, but they are associated with many limitations such as disturbances to the process flow, measurement delays, frequent need for maintenance, and high capital costs. As a result, soft sensors have become an attractive alternative for predicting quality-related parameters that are ‘hard-to-measure’ using hardware sensors. Due to their promising features over hardware counterparts, they have been employed across different process industries. This article attempts to explore the state-of-the-art artificial intelligence (Al)-driven soft sensors designed for process industries and their role in achieving the goal of sustainable development. First, a general introduction is given to soft sensors, their applications in different process industries, and their significance in achieving sustainable development goals. AI-based soft sensing algorithms are then introduced. Next, a discussion on how AI-driven soft sensors contribute toward different sustainable manufacturing strategies of process industries is provided. This is followed by a critical review of the most recent state-of-the-art AI-based soft sensors reported in the literature. Here, the use of powerful AI-based algorithms for addressing the limitations of traditional algorithms, that restrict the soft sensor performance is discussed. Finally, the challenges and limitations associated with the current soft sensor design, application, and maintenance aspects are discussed with possible future directions for designing more intelligent and smart soft sensing technologies to cater the future industrial needs

    Faludi Blogging

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    “Chasing Territorialism” gathers short texts by Emeritus Professor Andreas Faludi, originally written as blog posts over a period of two years. In Andreas’ words: “Stimulated by an, albeit brief, encounter with Albania celebrating Europe Day, I began blogging about the continuing relevance of criticising territorialism, as I’d done in The Poverty of Territorialism (Faludi 2018; Edgar Elgar), in particular - but not exclusively - in relation to European integration.” Here, territorialism stands for states claiming a monopoly on controlling their territories much as they try to control the loyalty of their citizens. As such, territorialism is a fundamental principle of political organisation. Continued reflection on the poverty of this principle has acquired urgent overtones with the resurgence of armed conflict in Europe and elsewhere. If anything, the general reaction to this and other continental and even global crises seems to be to further enforce territorialism. But, what if territorialism is the cause of, rather than the solution to our problems? If so, would heeding the call for determined state action not become a case of: ‘Out of the frying pan and into the fire’? This book does not give an answer. What it hopefully does is stimulate debate about what the answer should be

    Vulnerability of the Nigerian coast and communities to climate change induced coastal erosion

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    Improving coastal resilience to climate change hazards requires understanding past shoreline changes. As the coastal population grows, evaluation and monitoring of shoreline changes are essential for planning and development. Population growth increases exposure to sea level rise and coastal hazards. Nigeria, where the study is situated, is among the top fifteen countries in the world for coastal population exposure to sea level rise. This study provided a novel lens in establishing a link between social factors and the intensifying coastal erosion along the Akwa Ibom State study coast. The mixed-method approach used in the study to assess the vulnerability of the Nigerian coast and communities to climate change-induced coastal erosion proved to be essential in gathering a wide range of data (physical, socio economic, participatory GIS maps and social learning) that contributed to a more robust and holistic assessment of coastal erosion, which is a complex issue due to the interplay between the human and natural environments. Remotely sensed data was used to examine the susceptibility and coastal evolution of Akwa Ibom State over 36 years (1984 -2020). Longer-term (1984- 2020) and short-term (2015-2020) shoreline change analyses were used to understand coastal erosion and accretion. From 1984-2020, the total average linear regression rate (LRR) was - 2.7+0.18m/yr and from 2015-2020, it was -3.94 +1.28m/yr, demonstrating an erosional trend along the study coast. Although the rate of erosion varies along the study coast, the linear regression rates (LRR) results show a predominant trend of erosion in both the short and longer term. According to the 2022 Intergovernmental Panel on Climate Change report, loss of land, loss of assets, community disruption and livelihood, loss of environmental resources, ecosystem, loss of life, or adverse health impact are all potential risks along the African coast due to climate change – this study shows that these risks are already occurring today. To quantify the anticipated future coastal erosion risk by 2040 along the study coast, the findings in this study show an overall average LRR of -2.73+ 0.99 m/yr which anticipates that coastal erosion will still be prevalent along the coast by 2040. And, given the current global climate change situation, should be expected to be much higher than the current forecasting. This study re-conceptualised the European Environmental Agency Driver-Pressure StateImpact-Response (DPSIR) model to show Hazard-Driver-Pressure-State-Impact ResponseObservation causal linkages to coastal erosion hazards. The results showed how human activities and environmental interactions have evolved through time, causing coastal erosion. Removal of vegetation cover/backstop for residential and agricultural purposes, indicate that human activities significantly contribute to the study area's susceptibility, rapid shoreline changes, and vulnerability to coastal erosion, in addition to oceanic and climate change drivers such as sea level rise and storminess. Risk perception of coastal erosion in the study area was analysed using the rhizoanalytic method proposed by Deleueze. The method demonstrates how connections and movements can be related and how data can be used to show multiplicity, mark and unmark ideas, rupture pre-conceptions and make new connections. This study shows that coastal erosion awareness is insufficient to build a long-term management plan and sustain coastal resilience. The Hino's conceptual model which provides in-depth understanding on planned retreat was used to illustrate migratory and planned retreat for the study coast where relocation has already occurred due to coastal erosion. The result fell within the Self-Reliance quadrant, indicating that people left the risk zone without government backing or retreat plans. Other coastal residents who have not relocated fell within the Hunkered Down quadrant, showing that they are willing to stay in the risk zone and cope with the threat unless the government/environmental agencies relocate them. This study shows that coastal resilience requires adaptive capacity and government support. However, multilevel governance has inhibited government-community dialogue and involvement, increasing coastal erosion vulnerability. The coastal vulnerability index to coastal erosion was calculated using the Analytical Hierarchy Process weightings. It revealed that 67.55% of the study coast falls within the high-very high vulnerability class while 32.45% is within the very low-low vulnerability class. This study developed and combined a risk perception index to coastal erosion (RPIerosion) and participatory GIS (PGIS) mapping into a novel coastal vulnerability index called the integrated coastal erosion vulnerability index (ICEVI). The case study evaluation in Akata, showed an improvement in the overall vulnerability assessment to reflect the real-world scenario, which was consistent with field data. This study demonstrated not only the presence and challenges of coastal erosion in the research area but also the relevance of involvement between the local stakeholders, government and environmental agencies. Thus, showing the potential for the perspectives of the inhabitants of these regions to inform the understanding of the resilience capacity of the people impacted, and importantly to inform future co-design and/or selection of effective adaptation methods, to better support coastal climate change resilience in these communities. Overall, the study provides a useful contribution to coastal erosion vulnerability assessments in data-scarce regions more broadly, where the mixed-methods approach used here can be applied elsewhere

    The Russian Empire, Slaving and Liberation, 1480–1725

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    The monograph realigns political culture and countermeasures against slave raids, which rose during the breakup of the Golden Horde. By physical defense of the open steppe border and embracing the New Israel symbolism (exodus from slavery in Egypt/among the Tatars), Muscovites found a defensive model to expand the empire. Recent debates on slaving are introduced to Russian and imperial history, while challenging entrenched perceptions of Muscovy

    A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling

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    The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently

    Self-supervised learning techniques for monitoring industrial spaces

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    Dissertação de mestrado em Matemática e ComputaçãoEste documento é uma Dissertação de Mestrado com o título ”Self-Supervised Learning Techniques for Monitoring Industrial Spaces”e foi realizada e ambiente empresarial na empresa Neadvance - Machine Vision S.A. em conjunto com a Universidade do Minho. Esta dissertação surge de um grande projeto que consiste no desenvolvimento de uma plataforma de monitorização de operações específicas num espaço industrial, denominada SMARTICS (Plataforma tecnoló gica para monitorização inteligente de espaços industriais abertos). Este projeto continha uma componente de investigação para explorar um paradigma de aprendizagem diferente e os seus métodos - self-supervised learning, que foi o foco e principal contributo deste trabalho. O supervised learning atingiu um limite, pois exige anotações caras e dispendiosas. Em problemas reais, como em espaços industriais nem sempre é possível adquirir um grande número de imagens. O self-supervised learning ajuda nesses problemas, ex traindo informações dos próprios dados e alcançando bom desempenho em conjuntos de dados de grande escala. Este trabalho fornece uma revisão geral da literatura sobre a estrutura de self-supervised learning e alguns métodos. Também aplica um método para resolver uma tarefa de classificação para se assemelhar a um problema em um espaço industrial.This document is a Master’s Thesis with the title ”Self-Supervised Learning Techniques for Monitoring Industrial Spaces” and was carried out in a business environment at Neadvance - Machine Vision S.A. together with the University of Minho. This dissertation arises from a major project that consists of developing a platform to monitor specific operations in an industrial space, named SMARTICS (Plataforma tecnológica para monitorização inteligente de espaços industriais abertos). This project contained a research component to explore a different learning paradigm and its methods - self-supervised learning, which was the focus and main contribution of this work. Supervised learning has reached a bottleneck as they require expensive and time-consuming annotations. In real problems, such as in industrial spaces it is not always possible to require a large number of images. Self-supervised learning helps these issues by extracting information from the data itself and has achieved good performance in large-scale datasets. This work provides a comprehensive literature review of the self supervised learning framework and some methods. It also applies a method to solve a classification task to resemble a problem in an industrial space and evaluate its performance

    Generalised Symmetries and String Theory

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    In this thesis, we study the geometric origin of discrete higher-form symmetries and associated anomalies of dd-dimensional quantum field theories in terms of defect groups via geometric engineering in M-theory and type IIB string theory by reduction on non-compact spaces XX. As a warm-up, we analyze the example of 7d \cN=1 SYM theory, where we recover it from a mixed 't Hooft anomaly among the electric 1-form centre symmetry and the magnetic 4-form centre symmetry in the defect group. The case of 5-dimensional SCFTs from M-theory on toric singularities is discussed in detail. In that context, we determine the corresponding 1-form and 2-form defect groups and we explain how to determine the corresponding mixed 't Hooft anomalies from flux non-commutativity. For these theories, we further determine the d+1d+1 dimensional Symmetry TFT, or SymTFT for short, by reducing the topological sector of 11d supergravity on the boundary X\partial X of the space XX. Central to this endeavour is a reformulation of supergravity in terms of differential cohomology, which allows the inclusion of torsion in the cohomology of the space X\partial X, which in turn gives rise to the background fields for discrete symmetries. We further extend our analysis to study the 1-form symmetries of 4-dimensional \cN=2 supersymmetric quantum field theories which arise from IIB on hypersurface singularities. The examples we discuss include a broad class of \cN=2 theories such as Argyres-Douglas and Dpb(G)D_p^b(G) theories. In our computation of the defect groups of hypersurface singularities, we rely on a fundamental result in singularity theory known as Milnor's theorem which establishes a connection between the topology of the hypersurface and the local behaviour of the singularity. For the Dpb(G)D_p^b(G) theories, in the simple case when b=h(G)b=h^\vee (G), we use the BPS quivers of the theory to see that the defect group is compatible with a known Maruyoshi-Song flow. To extend to the case where bh(G)b\neq h^\vee (G), we use a similar Maruyoshi-Song flow to conjecture that the defect groups of Dpb(G)D_p^b(G) theories are given by those of G(b)[k]G^{(b)}[k] theories. In the cases of G=An,  E6,  E8G=A_n, \;E_6, \;E_8 we cross-check our result by calculating the BPS quivers of the G(b)[k]G^{(b)}[k] theories and looking at the cokernel of their intersection matrix

    k-Means

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