12,178 research outputs found
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
The State of the Art in Deep Learning Applications, Challenges, and Future Prospects::A Comprehensive Review of Flood Forecasting and Management
Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure
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Production networks in the cultural and creative sector: case studies from the publishing industry
The CICERONE project investigates cultural and creative industries through case study research, with a focus on production networks. This report, part of WP2, examines the publishing industry within this framework. It aims to understand the industryâs hidden aspects, address statistical issues in measurement, and explore the industryâs transformation and integration of cultural and economic values. The report provides an overview of the production network, explores statistical challenges, and presents qualitative analyses of two case studies. It concludes by highlighting the potential of the Global Production Network (GPN) approach for analyzing, researching, policymaking, and intervening in the European publishing network.
The CICERONE projectâs case study research delves into the publishing industry, investigating its production networks and examining key aspects often unseen by the public. The report addresses statistical challenges in measuring the industry and sheds light on its ongoing transformations and integration of cultural and economic values. It presents an overview of the production network, explores statistical issues, and provides qualitative analyses of two case studies. The report emphasizes the potential of the GPN approach for analyzing and intervening in the European publishing network, ultimately contributing to research, policymaking, and understanding within the industry
Runway Safety Improvements Through a Data Driven Approach for Risk Flight Prediction and Simulation
Runway overrun is one of the most frequently occurring flight accident types threatening the safety of aviation. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors.
To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach.
A series of experiments are performed to observe how the model accuracy change with different data pre-processing methods for the prediction model and different neural network models for the simulation model. The data pre-processing methods include indexing the data by different frequencies, by different window sizes, and data clustering. The neural network models include simple Recurrent Neural Networks, Gated Recurrent Units, Long Short Term Memory, and Neural Network Autoregressive with Exogenous Input. Another series of experiments are performed to evaluate the robustness of these models to adverse wind and flare. This is because different wind conditions and flares represent controls that the models need to map to the predicted flight states. The most robust models are then used to identify significant features for the prediction model and the feasible control space for the simulation model. The outcomes of the most robust models are also mapped to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, and high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk.
The main contribution of this work is on evaluating the accuracy and robustness of prediction and simulation models trained using Flight Operational Quality Assurance (FOQA) data. Unlike many studies that focused on optimizing the model structures to create the two models, this work optimized both data and model structures to ensure that the data well capture the dynamics of the aircraft it represents. To achieve this, this work introduced a hybrid genetic algorithm that combines the benefits of conventional and quantum-inspired genetic algorithms to quickly converge to an optimal configuration while exploring the design space. With the optimized model, this work identified the data features, from the final approach, with a higher contribution to predicting airspeed, vertical speed, and pitch angle near touchdown. The top contributing features are altitude, angle of attack, core rpm, and air speeds. For both the prediction and the simulation models, this study goes through the impact of various data preprocessing methods on the accuracy of the two models. The results may help future studies identify the right data preprocessing methods for their work. Another contribution from this work is on evaluating how flight control and wind affect both the prediction and the simulation models. This is achieved by mapping the model accuracy at various levels of control surface deflection, wind speeds, and wind direction change. The results saw fairly consistent prediction and simulation accuracy at different levels of control surface deflection and wind conditions. This showed that the neural network-based models are effective in creating robust prediction and simulation models of aircraft during the final approach. The results also showed that data frequency has a significant impact on the prediction and simulation accuracy so it is important to have sufficient data to train the models in the condition that the models will be used. The final contribution of this work is on demonstrating how the prediction and the simulation models can be used to increase awareness of runway overrun.Ph.D
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
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
Essays on Manufacturersâ IT Capabilities for Digital Servitization
Over the last decades, studies have found that transformational drivers affect how firms innovate their business models (Chesbrough, 2010; Massa et al., 2016). In markets in which physical products become commodities, the servitization of business models is a transformational driver for firms (Wise & Baumgartner, 1999). For its part, digitalization increases the potential to reshape business models through novel use cases of technology (Yoo et al., 2010). Recently, digitalization was found to extend the opportunities from servitization through digital technologies as digital servitization (Paschou et al., 2020). Digital servitization describes a firmâs shift from product-centric offerings to service-centric offerings with the help of novel IT assets (Naik et al., 2020). The manufacturing industry provides promising examples of firms with portfolios of physical offerings that might undergo such a transformational shift (Baines et al., 2017).
So far, digital servitization research focuses primarily on four topics: re-defining the notion of servitization in the context of digitalization, identifying digital servitization value drivers, linking the transformation to specific technologies, and deriving how novel service offerings arise (Paschou et al., 2020; Zhou & Song, 2021).
Despite the breadth of digital servitization research, how firms can shift to service-centric offerings remains unclear (KohtamÀki et al., 2019). Specifically, research lacks studies on the prerequisites and mechanisms that link theory with evidence on achieving IT-enabled service innovation (Paschou et al., 2020). Further, how firms must organize to build and operate IT-enabled services around these technologies remains unclear (Paschou et al., 2020). In a recent report on the manufacturing industry, practitioners confirm these gaps and associate them with a lack of managerial and technical knowledge (Illner et al., 2020).
A theoretical lens that helps to address these shortcomings is the knowledge-based theory. It suggests that knowledge is the primary rationale, so that a firm benefits from its assets (Grant, 1996b; Nonaka, 1994). The knowledge-based theory understands a capability as a directed application of knowledge in a firmâs activities (Grant, 1996b; Nonaka, 1994). In the context of digitalization, firms require IT capabilities based on knowledge of how to capitalize on IT assets (Lee et al., 2015). Digital servitization research finds that IT capabilities are critical for identifying, adapting, and exploiting IT-enabled service innovations (Johansson et al., 2019). Still, little extant research informs firms that undergo digital servitization about which IT capabilities can help to strengthen their competitive advantage (Coreynen et al., 2017).
Even though IT capabilities may be necessary for success in innovating IT-enabled services, the required knowledge needs to be disseminated effectively throughout an organization (Foss et al., 2014; Grant, 1996a; Nonaka, 1994). The organizational control theory offers a theoretical perspective about knowledge dissemination mechanisms, which can be horizontal or vertical (Ouchi, 1979). Horizontal knowledge dissemination mechanisms depend on codifying processes in rules or measuring process outputs through indicators, while the locus of exerting these rules and indicators determines the vertical knowledge dissemination. The IT innovation and IT governance literature refers to these knowledge dissemination mechanisms as formalization of IT activities and centralization of IT decision-making (Weill, 2004; Winkler & Brown, 2013; Zmud, 1982). However, how to orchestrate knowledge, particularly for IT capabilities, in firms that undergo digital servitization is not yet clear (KohtamĂ€ki et al., 2019; MĂŒnch et al., 2022; Sjödin et al., 2020).
Against this background, this dissertation addresses how manufacturers organize their IT capabilities while encountering the transformational drivers of digital servitization by answering the following overarching research question:
How can manufacturers organize their IT capabilities to capitalize on digital servitization? (References to be found in the full text):List of abbreviations in synopsis............................................................................................................V
Part I: Synopsis of the dissertation..........................................................................................................11
Motivation.......................................................................................................................................12
Research design...............................................................................................................................22.
1Conceptual approach and research objectives....................................................................22.
2Research methodologies and methods................................................................................4
3Structure of the dissertation.............................................................................................................5
3.1Systematization of the papers.............................................................................................5
3.2Paper1: Revisiting the concept of IT capabilities in the era of digitalization....................7
3.3Paper2: Short and sweet âMultiple mini case studies as a form of rigorous case studyresearch...............................................................................................................................9
3.4Paper3: Linking IT capabilities and competitive advantage of servitized business models..........................................................................................................................................11
3.5Paper4: From selling machinery to hybrid offerings âOrganizational impact of digitalservitization on manufacturing firms................................................................................11
3.6Paper5: Manufacturersâ IT-enabled service innovation success as a multifacetedphenomenon: A configurational study..............................................................................13
3.7Paper6: The missing piece âCalibration of qualitative data for qualitative comparativeanalyses in IS research......................................................................................................14
3.8Paper7: Prerequisites and causal recipes for manufacturersâ success in innovating ITenabled services................................................................................................................16
4Conclusion.....................................................................................................................................19
4.1Resultssummary...............................................................................................................19
4.2Contributions....................................................................................................................20
4.2.1Theoretical contributions......................................................................................20
4.2.2Methodological contribution................................................................................21
4.2.3Practical contribution............................................................................................21
4.3Limitations and future research........................................................................................22
5References.....................................................................................................................................24
Part II: Papers of the dissertation...........................................................................................................29
Paper1: Revisiting the concept of IT capabilities in the era of digitalization.......................................30
Paper2: Short and sweet âMultiple mini case studies as a form of rigorous case study research.......41
Paper3: Linking IT capabilities and competitive advantage of servitized business model..................64
Paper4: From selling machinery to hybrid offerings âOrganizational impact of digital servitization on manufacturing firms......................................................................................................................80
Paper5: Manufacturersâ IT-enabled service innovation success as a multifaceted phenomenon: A configurational study...................................................................................................................108
Paper6: The missing piece âCalibration of qualitative data for qualitative comparative analyses in IS research........................................................................................................................................119
Paper7: Prerequisites and causal recipes for manufacturersâ success in innovating IT-enabled services.....................................................................................................................................................136
Overview of the digital appendix on CD.............................................................................................17
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
Marine Data Fusion for Analyzing Spatio-Temporal Ocean Region Connectivity
This thesis develops methods to automate and objectify the connectivity analysis between ocean regions. Existing methods for connectivity analysis often rely on manual integration of expert knowledge, which renders the processing of large amounts of data tedious. This thesis presents a new framework for Data Fusion that provides several approaches for automation and objectification of the entire analysis process. It identifies different complexities of connectivity analysis and shows how the Data Fusion framework can be applied and adapted to them. The framework is used in this thesis to analyze geo-referenced trajectories of fish larvae in the western Mediterranean Sea, to trace the spreading pathways of newly formed water in the subpolar North Atlantic based on their hydrographic properties, and to gauge their temporal change. These examples introduce a new, and highly relevant field of application for the established Data Science methods that were used and innovatively combined in the framework. New directions for further development of these methods are opened up which go beyond optimization of existing methods. The Marine Science, more precisely Physical Oceanography, benefits from the new possibilities to analyze large amounts of data quickly and objectively for its exact research questions. This thesis is a foray into the new field of Marine Data Science. It practically and theoretically explores the possibilities of combining Data Science and the Marine Sciences advantageously for both sides. The example of automating and objectifying connectivity analysis between marine regions in this thesis shows the added value of combining Data Science and Marine Science. This thesis also presents initial insights and ideas on how researchers from both disciplines can position themselves to thrive as Marine Data Scientists and simultaneously advance our understanding of the ocean
âNot the story you want, Iâm sureâ: Mental health recovery and the narratives of people from marginalised communities
Background: The dominant narrative in mental health policy and practice has shifted in the 21st century from one of chronic ill health or incurability to an orientation towards recovery. A recovery-based approach is now the most frequently used in services in the Global North, and its relevance has also been explored in Global South settings. Despite the ubiquity of the recovery approach, people experiencing poverty, homelessness, intersecting oppressions (based for example on race, ethnicity, gender, sexuality or ability), and other forms of social marginalisation remain under-represented within recovery-oriented research. More inclusive research has been called for to ensure that knowledge of recovery processes is not based solely on the experiences of the relatively well-resourced.
Personal narratives of recovery from mental distress have played a central role in the establishment of the recovery approach within mental health policy and practice. Originating in survivor/service-user movements, the use of ârecovery narrativesâ has now become widespread for diverse purposes, including staff training to improve service delivery and increase empathy, public health campaigns to challenge stigma, online interventions to increase access to self-care resources, and as a distinctive feature of peer support. Research suggests that recovery-focused narratives can have benefits and also risks for narrators and recipients. At the same time, the elicitation of such narratives by healthcare researchers, educators and practitioners has been problematised by survivor-researchers and other critical theorists, as a co-option of lived experience for neoliberal purposes.
Following a systematic review of empirical research studies undertaken on characteristics of recovery narratives (presented in Chapter 4), a need for empirical research on the narratives of people from socially marginalised groups was identified. What kinds of stories might we/they be telling, and what are their experiences of telling their stories? What do their experiences tell us about the use of stories within a recovery approach?
Aim: Drawing on a body of critical scholarship, my aim is to conduct an empirical inquiry into (i) characteristics of recovery stories told by people from socially marginalised groups, and (ii) their experiences of telling their stories in formal and everyday settings.
Method: I undertook a critical narrative inquiry based on the stories of 77 people from marginalised groups, collected in the context of a wider study. This comprised narratives from people with lived experience of mental distress who additionally met one or more of the following criteria: (i) had experiences of psychosis; (ii) were from Black, Asian and other minoritised ethnic communities; (iii) are under-served by services (operationalised as lesbian, gay, bi, trans, queer + communities (LGBTQ+) or people identified as having multiple and complex needs); or (iv) had peer support roles. Two-part interviews were conducted (18 conducted by me). Part A consisted of an open-ended question designed to elicit a narrative, and part B was a semi-structured interview inviting participants to reflect on their experiences of telling their recovery stories in different contexts. Following Riessmanâs analytical approach, I undertook three forms of analysis: a structural narrative analysis of Part A across the dataset (informed by a preliminary conceptual framework developed in Chapter 4); a thematic analysis of Part B where participants additionally reflected on telling their stories; and an in-depth performative narrative analysis of two accounts (parts A and B) from people with multiple and complex needs.
Findings: In a structural analysis of Part A, the recovery narratives told by people from marginalised groups were found to be diverse and multidimensional. Most (97%) could be characterised by the nine dimensions described in the preliminary conceptual framework (Genre; Positioning; Emotional Tone; Relationship with Recovery; Trajectory; Turning Points; Narrative Sequence; Protagonists; and Use of Metaphors). Each dimension of the framework contained a number of different types. These were expanded as a result of the structural analysis to contain more types: for example, a âcyclicalâ type of trajectory was added), and a more comprehensive typology of recovery narratives was produced. Two narratives were found to be âoutliersâ, in that their structure, form and content could not adequately be described by the majority of existing dimensions and types. These served as exemplars of the frameworkâs limitations.
In a thematic analysis of Part B, my overarching finding was that power differentials between narrators and recipients could be seen as the key factor affecting participantsâ experiences of telling their recovery stories in formal and everyday settings. Four themes describing the possibilities and problems raised by telling their stories were identified: (i) âChallenging the status quoâ; (ii) âRisky consequencesâ; (iii) âProducing acceptable storiesâ and (iv) âUntellable storiesâ.
In a performative analysis of two narratives of people with multiple and complex needs (Parts A and B), I found two contrasting ways of responding to the invitation to tell a recovery story: a ânarrative of personal lackâ and a ânarrative of resistanceâ. I demonstrate how the genre of ârecovery narrativeâ, with its focus on transformation at the level of personal identity, may function to occlude social and structural causes of distress, and reinforce ideas of personal responsibility for ongoing distress in the face of unchanging living conditions.
Conclusion: The recovery narratives of people from socially marginalised groups are diverse and multidimensional. Told in some contexts, they may hold power to challenge the status quo. However, telling stories of lived experience and recovery is risky, and there may be pressure on narrators to produce âacceptableâ stories, or to omit or de-emphasise experiences which challenge dominant cultural narratives. A recovery-based approach to the use of lived experience narratives in research and practice may be contributing towards an over-emphasis on individualist approaches to the reduction of distress. This over-emphasis can be seen to reflect what has been identified as a global trend towards the âinstrumentalâ use of personal narratives for utilitarian purposes based on market values. Attention to power differentials and structural as well as agentic factors is vital to ensure that the use of narratives in research and practice does not contribute towards a decontextualised, reductionist form of recovery which pays insufficient attention to the economic, institutional and political injustices that people experiencing mental distress may systematically endure. A sensitive and socially just use of lived experience narratives will remain alert to a variety of power dimensions present within the contexts in which they are shared and hear
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