4,784 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

    Multi-epoch machine learning for galaxy formation

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    In this thesis I utilise a range of machine learning techniques in conjunction with hydrodynamical cosmological simulations. In Chapter 2 I present a novel machine learning method for predicting the baryonic properties of dark matter only subhalos taken from N-body simulations. The model is built using a tree-based algorithm and incorporates subhalo properties over a wide range of redshifts as its input features. I train the model using a hydrodynamical simulation which enables it to predict black hole mass, gas mass, magnitudes, star formation rate, stellar mass, and metallicity. This new model surpasses the performance of previous models. Furthermore, I explore the predictive power of each input property by looking at feature importance scores from the tree-based model. By applying the method to the LEGACY N-body simulation I generate a large volume mock catalog of the quasar population at z=3. By comparing this mock catalog with observations, I demonstrate that the IllustrisTNG subgrid model for black holes is not accurately capturing the growth of the most massive objects. In Chapter 3 I apply my method to investigate the evolution of galaxy properties in different simulations, and in various environments within a single simulation. By comparing the Illustris, EAGLE, and TNG simulations I show that subgrid model physics plays a more significant role than the choice of hydrodynamics method. Using the CAMELS simulation suite I consider the impact of cosmological and astrophysical parameters on the buildup of stellar mass within the TNG and SIMBA models. In the final chapter I apply a combination of neural networks and symbolic regression methods to construct a semi-analytic model which reproduces the galaxy population from a cosmological simulation. The neural network based approach is capable of producing a more accurate population than a previous method of binning based on halo mass. The equations resulting from symbolic regression are found to be a good approximation of the neural network

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (ā€˜AIā€™) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics ā€“ and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the CatĆ³lica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Use of Patient Diaries in pAediatric inTensive carE: UPDATE Study. A Constructivist Grounded Theory Study exploring the child, parents, and healthcare professionalsā€™ perspectives.

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    Use of Patient Diaries in pAediatric inTensive carE: UPDATE Study. A Constructivist Grounded Theory Study exploring the child, parents, and healthcare professionalsā€™ perspectives. Fiona Lynch Paediatric Intensive Care Units (PICU) are essential to paediatric healthcare, providing high medical and nursing care to children who have become critically ill. PICUs offer prompt and appropriate interventions for children who have developed physiological instability, whether following surgery, from infection, trauma, or deterioration of a chronic condition. A stay in a PICU is not without potential psychological consequences for the child and family. Interventions to support recovery from the psychological impact of critical illness for the child and their family are evolving. Studies undertaken in adult and childrenā€™s intensive care have explored interventions that may reduce the psychological impact of critical illness for the patient and their relatives, such as patient diaries. The impact of patient diaries is still emerging in PICUs, and a clear picture of how this intervention is used is the logical next step. Therefore, this study aimed to ascertain how PICU patient diaries are used by critically ill children, their families, and healthcare professionals during and after admission into the PICU. Constructivist grounded theory (Charmaz, 2006) was identified as the most appropriate methodology to study how the child, their family and the teams caring for them used patient diaries. The methods of intensive interviews and focus group interviews were adopted to generate rich data from the participants. Two intensive consecutive interviews were conducted with children and their family. The first interviews were conducted during the admission to the PICU and then repeated approximately five to six months post-discharge from the hospital. In total, 11 interviews were conducted during the admission to the PICU and six interviews post-discharge from the PICU. Only one child was interviewed as a participant in this study. Five separate focus group interviews were conducted with healthcare professionals to ascertain their views on the use and usefulness of the diaries. In total, 95 HCPs were recruited for the five focus group interviews, with four conducted in the PICU and one in the childrenā€™s cardiology ward. Two categories evolved from the family participants and three categories from the HCPs, leading to the development of the core category of Making Sense. Findings showed that patient diaries provided a communication tool which strengthened relationships between the parent and their child, the healthcare professionals and other family members by Creating Connections. The relationships fostered through the diaries were viewed as Impacting Emotionally on parents and HCPs. From this emotional involvement, the diary was considered to support the parentsā€™ emotional wellbeing. From the entries made by the family members, the diaries provided insights into how families coped emotionally, allowing HCPs to provide individualised support where needed. In an environment with an imbalance of power and unfamiliar organisational processes and cultures, the diary supported parental autonomy by Empowering Involvement to make decisions. The patient diary is a tool to bridge the knowledge gap between parents and HCPs in the childā€™s critical illness experience. Through this use, the diary offered a compendium of information about the childā€™s PICU journey. Providing clear insights and explanations of their child's PICU admission, the patient diary filled any gaps in memory and offered an easily understandable permanent record. Therefore, the diary was a valuable resource supporting Making Sense of the childā€™s complex critical illness journey. The UPDATE study has explored the uses of PICU patient diaries from the perspective of the families and HCPs. The patient diaries were valued and offered a tool to support the family, the HCP and child in making sense of the critical illness journey. Through CGT this study has provided insights not previously understood and contributes to the evolving evidence and theory about using patient diaries for the survivors of the PICU. ā€ƒFlorence Nightingale FoundationUniversity Hospitals of Susse

    The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

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    In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

    Evaluating the sustainability and resiliency of local food systems

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    With an ever-rising global population and looming environmental challenges such as climate change and soil degradation, it is imperative to increase the sustainability of food production. The drastic rise in food insecurity during the COVID-19 pandemic has further shown a pressing need to increase the resiliency of food systems. One strategy to reduce the dependence on complex, vulnerable global supply chains is to strengthen local food systems, such as by producing more food in cities. This thesis uses an interdisciplinary, food systems approach to explore aspects of sustainability and resiliency within local food systems. Lifecycle assessment (LCA) was used to evaluate how farm scale, distance to consumer, and management practices influence environmental impacts for different local agriculture models in two case study locations: Georgia, USA and England, UK. Farms were grouped based on urbanisation level and management practices, including: urban organic, peri-urban organic, rural organic, and rural conventional. A total of 25 farms and 40 crop lifecycles were evaluated, focusing on two crops (kale and tomatoes) and including impacts from seedling production through final distribution to the point of sale. Results were extremely sensitive to the allocation of composting burdens (decomposition emissions), with impact variation between organic farms driven mainly by levels of compost use. When composting burdens were attributed to compost inputs, the rural conventional category in the U.S. and the rural organic category in the UK had the lowest average impacts per kg sellable crop produced, including the lowest global warming potential (GWP). However, when subtracting avoided burdens from the municipal waste stream from compost inputs, trends reversed entirely, with urban or peri-urban farm categories having the lowest impacts (often negative) for GWP and marine eutrophication. Overall, farm management practices were the most important factor driving environmental impacts from local food supply chains. A soil health assessment was then performed on a subset of the UK farms to provide insight to ecosystem services that are not captured within LCA frameworks. Better soil health was observed in organically-farmed and uncultivated soils compared to conventionally farmed soils, suggesting higher ecosystem service provisioning as related to improved soil structure, flood mitigation, erosion control, and carbon storage. However, relatively high heavy metal concentrations were seen on urban and peri-urban farms, as well as those located in areas with previous mining activity. This implies that there are important services and disservices on farms that are not captured by LCAs. Zooming out from a focus on food production, a qualitative methodology was used to explore experiences of food insecurity and related health and social challenges during the COVID-19 pandemic. Fourteen individuals receiving emergency food parcels from a community food project in Sheffield, UK were interviewed. Results showed that maintaining food security in times of crisis requires a diverse set of individual, household, social, and place-based resources, which were largely diminished or strained during the pandemic. Drawing upon social capital and community support was essential to cope with a multiplicity of hardship, highlighting a need to develop community food infrastructure that supports ideals of mutual aid and builds connections throughout the food supply chain. Overall, this thesis shows that a range of context-specific solutions are required to build sustainable and resilient food systems. This can be supported by increasing local control of food systems and designing strategies to meet specific community needs, whilst still acknowledging a shared global responsibility to protect ecosystem, human, and planetary health

    Introduction to Psychology

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    Introduction to Psychology is a modified version of Psychology 2e - OpenStax

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ļ¬fth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ļ¬elds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiļ¬ed Proportional Conļ¬‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiļ¬ers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiļ¬cation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiļ¬cation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiļ¬cation, and hybrid techniques mixing deep learning with belief functions as well

    Level-Set Mass-Conservative Front-Tracking Technique for Multistep Simulations of In-Flight Ice Accretion

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    This paper presents a novel level-set-based approach to model evolving boundary problems for in-flight ice accretion. No partial differential equations are solved as in the standard level-set formulation, but simple geometrical quantities are employed to provide an implicit discretization of the updated boundary. This method avoids mesh entanglements and grid intersections typical of algebraic and mesh deforming techniques, making it suitable for generating a body-fitted discretization of arbitrarily complex geometries as in-flight ice shapes, including the collision of separate ice fronts. Moreover, this paper presents a local ice thickness correction, which accounts for the body's curvature, to conserve the prescribed iced mass locally. The verification includes ice accretion over an ellipse and a manufactured example to show the proposed strategy's advantages and robustness compared to standard algebraic methods. Finally, the method is applied to ice accretion problems. A temporal and grid convergence study is presented for automatic multistep in-flight simulations over a NACA0012 airfoil in rime, glaze, and mixed ice conditions
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