4,126 research outputs found

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    A reinforcement learning recommender system using bi-clustering and Markov Decision Process

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    Collaborative filtering (CF) recommender systems are static in nature and does not adapt well with changing user preferences. User preferences may change after interaction with a system or after buying a product. Conventional CF clustering algorithms only identifies the distribution of patterns and hidden correlations globally. However, the impossibility of discovering local patterns by these algorithms, headed to the popularization of bi-clustering algorithms. Bi-clustering algorithms can analyze all dataset dimensions simultaneously and consequently, discover local patterns that deliver a better understanding of the underlying hidden correlations. In this paper, we modelled the recommendation problem as a sequential decision-making problem using Markov Decision Processes (MDP). To perform state representation for MDP, we first converted user-item votings matrix to a binary matrix. Then we performed bi-clustering on this binary matrix to determine a subset of similar rows and columns. A bi-cluster merging algorithm is designed to merge similar and overlapping bi-clusters. These bi-clusters are then mapped to a squared grid (SG). RL is applied on this SG to determine best policy to give recommendation to users. Start state is determined using Improved Triangle Similarity (ITR similarity measure. Reward function is computed as grid state overlapping in terms of users and items in current and prospective next state. A thorough comparative analysis was conducted, encompassing a diverse array of methodologies, including RL-based, pure Collaborative Filtering (CF), and clustering methods. The results demonstrate that our proposed method outperforms its competitors in terms of precision, recall, and optimal policy learning

    Advances in machine learning algorithms for financial risk management

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    In this thesis, three novel machine learning techniques are introduced to address distinct yet interrelated challenges involved in financial risk management tasks. These approaches collectively offer a comprehensive strategy, beginning with the precise classification of credit risks, advancing through the nuanced forecasting of financial asset volatility, and ending with the strategic optimisation of financial asset portfolios. Firstly, a Hybrid Dual-Resampling and Cost-Sensitive technique has been proposed to combat the prevalent issue of class imbalance in financial datasets, particularly in credit risk assessment. The key process involves the creation of heuristically balanced datasets to effectively address the problem. It uses a resampling technique based on Gaussian mixture modelling to generate a synthetic minority class from the minority class data and concurrently uses k-means clustering on the majority class. Feature selection is then performed using the Extra Tree Ensemble technique. Subsequently, a cost-sensitive logistic regression model is then applied to predict the probability of default using the heuristically balanced datasets. The results underscore the effectiveness of our proposed technique, with superior performance observed in comparison to other imbalanced preprocessing approaches. This advancement in credit risk classification lays a solid foundation for understanding individual financial behaviours, a crucial first step in the broader context of financial risk management. Building on this foundation, the thesis then explores the forecasting of financial asset volatility, a critical aspect of understanding market dynamics. A novel model that combines a Triple Discriminator Generative Adversarial Network with a continuous wavelet transform is proposed. The proposed model has the ability to decompose volatility time series into signal-like and noise-like frequency components, to allow the separate detection and monitoring of non-stationary volatility data. The network comprises of a wavelet transform component consisting of continuous wavelet transforms and inverse wavelet transform components, an auto-encoder component made up of encoder and decoder networks, and a Generative Adversarial Network consisting of triple Discriminator and Generator networks. The proposed Generative Adversarial Network employs an ensemble of unsupervised loss derived from the Generative Adversarial Network component during training, supervised loss and reconstruction loss as part of its framework. Data from nine financial assets are employed to demonstrate the effectiveness of the proposed model. This approach not only enhances our understanding of market fluctuations but also bridges the gap between individual credit risk assessment and macro-level market analysis. Finally the thesis ends with a novel proposal of a novel technique or Portfolio optimisation. This involves the use of a model-free reinforcement learning strategy for portfolio optimisation using historical Low, High, and Close prices of assets as input with weights of assets as output. A deep Capsules Network is employed to simulate the investment strategy, which involves the reallocation of the different assets to maximise the expected return on investment based on deep reinforcement learning. To provide more learning stability in an online training process, a Markov Differential Sharpe Ratio reward function has been proposed as the reinforcement learning objective function. Additionally, a Multi-Memory Weight Reservoir has also been introduced to facilitate the learning process and optimisation of computed asset weights, helping to sequentially re-balance the portfolio throughout a specified trading period. The use of the insights gained from volatility forecasting into this strategy shows the interconnected nature of the financial markets. Comparative experiments with other models demonstrated that our proposed technique is capable of achieving superior results based on risk-adjusted reward performance measures. In a nut-shell, this thesis not only addresses individual challenges in financial risk management but it also incorporates them into a comprehensive framework; from enhancing the accuracy of credit risk classification, through the improvement and understanding of market volatility, to optimisation of investment strategies. These methodologies collectively show the potential of the use of machine learning to improve financial risk management

    Communicating a Pandemic

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    This edited volume compares experiences of how the Covid-19 pandemic was communicated in the Nordic countries – Denmark, Finland, Iceland, Norway, and Sweden. The Nordic countries are often discussed in terms of similarities concerning an extensive welfare system, economic policies, media systems, and high levels of trust in societal actors. However, in the wake of a global pandemic, the countries’ coping strategies varied, creating certain question marks on the existence of a “Nordic model”. The chapters give a broad overview of crisis communication in the Nordic countries during the first year of the Covid-19 pandemic by combining organisational and societal theoretical perspectives and encompassing crisis response from governments, public health authorities, lobbyists, corporations, news media, and citizens. The results show several similarities, such as political and governmental responses highlighting solidarity and the need for exceptional measures, as expressed in press conferences, social media posts, information campaigns, and speeches. The media coverage relied on experts and was mainly informative, with few critical investigations during the initial phases. Moreover, surveys and interviews show the importance of news media for citizens’ coping strategies, but also that citizens mostly trusted both politicians and health authorities during the crisis. This book is of interest to all who are looking to understand societal crisis management on a comprehensive level. The volume contains chapters from leading experts from all the Nordic countries and is edited by a team with complementary expertise on crisis communication, political communication, and journalism, consisting of Bengt Johansson, Øyvind Ihlen, Jenny Lindholm, and Mark Blach-Ørsten. Publishe

    Air Quality Research Using Remote Sensing

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    Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic

    Automated identification and behaviour classification for modelling social dynamics in group-housed mice

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    Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results. This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing. A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups. The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup. Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic

    Les conséquences de la chasse au gros gibier chez deux omnivores opportunistes

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    La chasse peut entraĂźner plusieurs consĂ©quences chez les populations animales exploitĂ©es, incluant la sĂ©lection de certains traits comportementaux et des changements de comportement induits par un paysage de la peur. Les activitĂ©s de chasse peuvent ĂȘtre perçues comme une menace par les animaux qui ne sont pas ciblĂ©s et ces derniers modifient leur comportement de façon Ă  moduler leur exposition au risque perçu. Les consĂ©quences de la chasse ne se limitent donc pas qu’aux espĂšces ou groupes dĂ©mographiques ciblĂ©s par les activitĂ©s de chasse; cependant, peu d’études ont rĂ©ellement tentĂ© de documenter les effets de la chasse sur les espĂšces non ciblĂ©es et, plus particuliĂšrement, chez les carnivores qui peuvent autant percevoir les chasseurs comme une menace qu’une source d’accĂšs Ă  la nourriture. En effet, une pratique courante chez les chasseurs est d’éviscĂ©rer le gibier sur le site d’abattage, ce qui augmente considĂ©rablement la quantitĂ© de biomasse disponible pour les charognards. Toutefois, consommer cette ressource pourrait ĂȘtre dĂ©savantageux sur le long terme. La chasse est une importante source d’émission de pollution puisque la majoritĂ© des chasseurs de gros gibier utilisent des munitions en plomb. Ces munitions se fragmentent aprĂšs avoir atteint leur cible et incrustent des millions de fragments de plomb qui peuvent ensuite ĂȘtre ingĂ©rĂ©s par des charognards qui se nourrissent des restes d’abattage jetĂ©s par les chasseurs. En temps normal, il est avantageux d’adopter des comportements charognards, mais cela devient inadaptĂ© durant la pĂ©riode de chasse puisqu’une grande quantitĂ© de plomb se retrouve dans les restes d’abattage et que les charognards n’ont aucun moyen d’évaluer ce risque. L’objectif de ma thĂšse de doctorat Ă©tait de documenter les consĂ©quences de la chasse au gros gibier chez deux omnivores opportunistes. Mes travaux peuvent ĂȘtre divisĂ©s en deux grandes sections: une premiĂšre sur les effets de la chasse sur le comportement de l’ours brun (Ursus arctos) Scandinave et une deuxiĂšme sur le lien entre la distribution des sites d’abattage de gros gibier et le risque d’exposition au plomb chez l’ours brun Scandinave en SuĂšde et l’ours noir d’AmĂ©rique (Ursus americanus) au QuĂ©bec. Les diffĂ©rents chapitres de cette thĂšse ont pu ĂȘtre rĂ©alisĂ©s grĂące Ă  des collaborations avec le Scandinavian Brown Bear Research Project (SBBRP) et le ministĂšre des forĂȘts, de la faune et des parcs du QuĂ©bec. Le SBBRP rĂ©alise un suivi longitudinal de la population suĂ©doise d’ours bruns depuis 1985 et plusieurs individus sont munis d’un collier GPS permettant de suivre leurs mouvements. Dans le chapitre 2, j’ai documentĂ© la rĂ©ponse des ours bruns face Ă  la chasse Ă  l’orignal (Alces alces) en SuĂšde. J’ai montrĂ© que les ours Ă©vitent les sites d’abattage d’orignaux tant durant le jour que durant la nuit et qu’ils augmentent la sĂ©lection d’habitats moins favorables aux chasseurs durant les pĂ©riodes de chasse Ă  l’ours et Ă  l’orignal. Cela suggĂšre que les restes d’abattage n’ont pas un effet attractif chez les ours en SuĂšde et que ces derniers ne font pas la diffĂ©rence entre les chasseurs d’ours et les chasseurs d’orignaux, puisqu’ils adoptent des tactiques d’anti-prĂ©dation similaires durant les deux pĂ©riodes de chasse. Dans le chapitre 3, j’ai montrĂ© que la protection lĂ©gale contre la rĂ©colte n’avait pas d’impact au niveau de la perception du risque chez les groupes protĂ©gĂ©s. Cela n'est pas surprenant en soi, mais mes rĂ©sultats montrent que les femelles avec des jeunes dĂ©pendants se dĂ©placent plus rapidement lorsqu’elles sont prĂšs des routes durant les heures lĂ©gales de chasse. Cette rĂ©ponse pourrait augmenter les coĂ»ts de locomotion chez les femelles avec des jeunes dĂ©pendants, et ce, malgrĂ© la protection dont elles bĂ©nĂ©ficient. Dans les chapitres 4 et 5, j’ai montrĂ© que les concentrations de plomb dans les tissus de deux espĂšces d’ours varient en fonction de la distribution des sites d’abattage. Ces rĂ©sultats indiquent que les ours sont plus exposĂ©s au plomb dans les zones oĂč il y a plus de chasse. Pour le moment, nous ne savons pas si les augmentations observĂ©es sont suffisantes pour induire des effets dĂ©lĂ©tĂšres chez les ours, mais les effets nĂ©fastes du plomb peuvent ĂȘtre observĂ©s Ă  de trĂšs faibles concentrations. Il est donc possible que les chasseurs de gros gibier crĂ©ent un piĂšge Ă©volutif pour les mammifĂšres charognards comme c’est le cas pour les charognards aviaires. Dans le chapitre 5, j’ai aussi utilisĂ© une fonction de sĂ©lection de ressources afin de prĂ©dire la distribution des sites d’abattage d’orignaux Ă  l’intĂ©rieur de notre aire d’étude en SuĂšde. Cette utilisation novatrice de la fonction de sĂ©lection de ressources pourrait ĂȘtre aisĂ©ment rĂ©pliquĂ©e dans d’autres systĂšmes d’étude et ainsi amĂ©liorer nos connaissances sur le lien entre la distribution des sites d’abattage et le risque d’exposition au plomb provenant des munitions chez les charognards. À travers les diffĂ©rents chapitres de cette thĂšse, j’ai montrĂ© que la chasse pouvait entraĂźner des consĂ©quences variĂ©es chez les espĂšces ou les groupes dĂ©mographiques qui ne sont pas convoitĂ©s durant les activitĂ©s de chasse. Les chasseurs peuvent induire des rĂ©ponses anti-prĂ©datrices chez plusieurs espĂšces; ces rĂ©ponses peuvent ĂȘtre associĂ©es Ă  des coĂ»ts nutritionnels ou une augmentation des dĂ©penses Ă©nergĂ©tiques durant une pĂ©riode critique juste avant l’hiver. Les chasseurs sont aussi d’importants Ă©metteurs de plomb dans l’environnement, ce qui pose un risque pour la santĂ© des charognards qui se nourrissent des restes d’abattage jetĂ©s durant la pĂ©riode de chasse. L’exposition au plomb provenant de la chasse est un piĂšge Ă©volutif bien documentĂ© chez les charognards aviaires et j’ai montrĂ© que les mammifĂšres pouvaient aussi s’exposer au plomb de la mĂȘme façon. Si des effets dĂ©lĂ©tĂšres de cette exposition venaient Ă  ĂȘtre dĂ©tectĂ©s chez les mammifĂšres, le piĂšge Ă©volutif pourrait s’étendre Ă  d’autres groupes de charognards

    Innovation in Energy Security and Long-Term Energy Efficiency Ⅱ

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    The sustainable development of our planet depends on the use of energy. The increasing world population inevitably causes an increase in the demand for energy, which, on the one hand, threatens us with the potential to encounter a shortage of energy supply, and, on the other hand, causes the deterioration of the environment. Therefore, our task is to reduce this demand through different innovative solutions (i.e., both technological and social). Social marketing and economic policies can also play their role by affecting the behavior of households and companies and by causing behavioral change oriented to energy stewardship, with an overall switch to renewable energy resources. This reprint provides a platform for the exchange of a wide range of ideas, which, ultimately, would facilitate driving societies toward long-term energy efficiency

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    The ethics and politics of deportation in Europe

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    Defence date: 19 February 2019Examining Board: Professor Rainer Bauböck, European University Institute (Supervisor); Professor Matthew Gibney, University of Oxford; Professor Iseult Honohan, University College Dublin; Professor Jennifer Welsh, McGill University (formerly European University Institute)This thesis explores key empirical and normative questions prompted by deportation policies and practices in the contemporary European context. The core empirical research question the thesis seeks to address is: what explains the shape of deportation regimes in European liberal democracies? The core normative research question is: how should we evaluate these deportation regimes morally? The two parts of the thesis address each of these questions in turn. To explain contemporary European deportation regimes, the four chapters of the first part of the thesis investigate them from a historical and multilevel perspective. (“Expulsion Old and New”) starts by comparing contemporary deportation practices to earlier forms of forced removal such as criminal banishment, political exile, poor law expulsion, and collective expulsions on a religious or ethnic basis, highlighting how contemporary deportation echoes some of the purposes of these earlier forms of expulsion. (“Divergences in Deportation”) looks at some major differences between European countries in how, and how much, deportation is used as a policy instrument today, concluding that they can be roughly grouped into four regime types, namely lenient, selective, symbolically strict and coercively strict. The next two chapters investigate how non-national levels of government are involved in shaping deportation in the European context. (“Europeanising Expulsion”) traces how the institutions of the European Union have come to both restrain and facilitate or incentivise member states’ deportation practices in fundamental ways. (“Localities of Belonging”) describes how provincial and municipal governments are increasingly assertive in frustrating deportations, effectively shielding individuals or entire categories of people from the reach of national deportation efforts, while in other cases local governments pressure the national level into instigating deportation proceedings against unwanted residents. The chapters argue that such efforts on both the supranational and local levels must be explained with reference to supranational and local conceptions of membership that are part of a multilevel citizenship structure yet can, and often do, come apart from the national conception of belonging. The second part of the thesis addresses the second research question by discussing the normative issues deportation gives rise to. (“Deportability, Domicile and the Human Right to Stay”) argues that a moral and legal status of non-deportability should be extended beyond citizenship to all those who have established effective domicile, or long-term and permanent residence, in the national territory. (“Deportation without Domination?”) argues that deportation can and should be applied in a way that does not dominate those it subjects by ensuring its non-arbitrary application through a limiting of executive discretion and by establishing proportionality testing in deportation procedures. (“Resisting Unjust Deportation”) investigates what can and should be done in the face of unjust national deportation regimes, proposing that a normative framework for morally justified antideportation resistance must start by differentiating between the various individual and institutional agents of resistance before specifying how their right or duty to resist a particular deportation depends on motivational, epistemic and relational conditions
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