8,993 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
Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse
This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses.
This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups.
In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena
Tourism and heritage in the Chornobyl Exclusion Zone
Tourism and Heritage in the Chornobyl Exclusion Zone (CEZ) uses an ethnographic lens to explore the dissonances associated with the commodification of Chornobyl's heritage.
The book considers the role of the guides as experience brokers, focusing on the synergy between tourists and guides in the performance of heritage interpretation. Banaszkiewicz proposes to perceive tour guides as important actors in the bottom-up construction of heritage discourse contributing to more inclusive and participatory approach to heritage management. Demonstrating that the CEZ has been going through a dynamic transformation into a mass tourism attraction, the book offers a critical reflection on heritagisation as a meaning-making process in which the resources of the past are interpreted, negotiated, and recognised as a valuable legacy. Applying the concepts of dissonant heritage to describe the heterogeneous character of the CEZ, the book broadens the interpretative scope of dark tourism which takes on a new dimension in the context of the war in Ukraine.
Tourism and Heritage in the Chornobyl Exclusion Zone argues that post-disaster sites such as Chornobyl can teach us a great deal about the importance of preserving cultural and natural heritage for future generations. The book will be of interest to academics and students who are engaged in the study of heritage, tourism, memory, disasters and Eastern Europe
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Evaluating 3D human face reconstruction from a frontal 2D image, focusing on facial regions associated with foetal alcohol syndrome
Foetal alcohol syndrome (FAS) is a preventable condition caused by maternal alcohol consumption during pregnancy. The FAS facial phenotype is an important factor for diagnosis, alongside central nervous system impairments and growth abnormalities. Current methods for analysing the FAS facial phenotype rely on 3D facial image data, obtained from costly and complex surface scanning devices. An alternative is to use 2D images, which are easy to acquire with a digital camera or smart phone. However, 2D images lack the geometric accuracy required for accurate facial shape analysis. Our research offers a solution through the reconstruction of 3D human faces from single or multiple 2D images. We have developed a framework for evaluating 3D human face reconstruction from a single-input 2D image using a 3D face model for potential use in FAS assessment. We first built a generative morphable model of the face from a database of registered 3D face scans with diverse skin tones. Then we applied this model to reconstruct 3D face surfaces from single frontal images using a model-driven sampling algorithm. The accuracy of the predicted 3D face shapes was evaluated in terms of surface reconstruction error and the accuracy of FAS-relevant landmark locations and distances. Results show an average root mean square error of 2.62 mm. Our framework has the potential to estimate 3D landmark positions for parts of the face associated with the FAS facial phenotype. Future work aims to improve on the accuracy and adapt the approach for use in clinical settings.
Significance:
Our study presents a framework for constructing and evaluating a 3D face model from 2D face scans and evaluating the accuracy of 3D face shape predictions from single images. The results indicate low generalisation error and comparability to other studies. The reconstructions also provide insight into specific regions of the face relevant to FAS diagnosis. The proposed approach presents a potential cost-effective and easily accessible imaging tool for FAS screening, yet its clinical application needs further research
Evaluation of image quality and reconstruction parameters in recent PET-CT and PET-MR systems
In this PhD dissertation, we propose to evaluate the impact of using different PET isotopes for
the National Electrical Manufacturers Association (NEMA) tests performance evaluation of the
GE Signa integrated PET/MR. The methods were divided into three closely related categories:
NEMA performance measurements, system modelling and evaluation of the image quality of
the state-of-the-art of clinical PET scanners. NEMA performance measurements for
characterizing spatial resolution, sensitivity, image quality, the accuracy of attenuation and
scatter corrections, and noise equivalent count rate (NECR) were performed using clinically
relevant and commercially available radioisotopes. Then we modelled the GE Signa integrated
PET/MR system using a realistic GATE Monte Carlo simulation and validated it with the result of
the NEMA measurements (sensitivity and NECR). Next, the effect of the 3T MR field on the
positron range was evaluated for F-18, C-11, O-15, N-13, Ga-68 and Rb-82. Finally, to evaluate the image
quality of the state-of-the-art clinical PET scanners, a noise reduction study was performed
using a Bayesian Penalized-Likelihood reconstruction algorithm on a time-of-flight PET/CT
scanner to investigate whether and to what extent noise can be reduced. The outcome of this
thesis will allow clinicians to reduce the PET dose which is especially relevant for young
patients. Besides, the Monte Carlo simulation platform for PET/MR developed for this thesis will
allow physicists and engineers to better understand and design integrated PET/MR systems
Visualisation of Fundamental Movement Skills (FMS): An Iterative Process Using an Overarm Throw
Fundamental Movement Skills (FMS) are precursor gross motor skills to more complex or specialised skills and are recognised as important indicators of physical competence, a key component of physical literacy. FMS are predominantly assessed using pre-defined manual methodologies, most commonly the various iterations of the Test of Gross Motor Development. However, such assessments are time-consuming and often require a minimum basic level of training to conduct. Therefore, the overall aim of this thesis was to utilise accelerometry to develop a visualisation concept as part of a feasibility study to support the learning and assessment of FMS, by reducing subjectivity and the overall time taken to conduct a gross motor skill assessment. The overarm throw, an important fundamental movement skill, was specifically selected for the visualisation development as it is an acyclic movement with a distinct initiation and conclusion. Thirteen children (14.8 ± 0.3 years; 9 boys) wore an ActiGraph GT9X Link Inertial Measurement Unit device on the dominant wrist whilst performing a series of overarm throws. This thesis illustrates how the visualisation concept was developed using raw accelerometer data, which was processed and manipulated using MATLAB 2019b software to obtain and depict key throw performance data, including the trajectory and velocity of the wrist during the throw. Overall, this thesis found that the developed visualisation concept can provide strong indicators of throw competency based on the shape of the throw trajectory. Future research should seek to utilise a larger, more diverse, population, and incorporate machine learning. Finally, further work is required to translate this concept to other gross motor skills
Targeting Fusion Proteins of HIV-1 and SARS-CoV-2
Viruses are disease-causing pathogenic agents that require host cells to replicate. Fusion of host and viral membranes is critical for the lifecycle of enveloped viruses. Studying viral fusion proteins can allow us to better understand how they shape immune responses and inform the design of therapeutics such as drugs, monoclonal antibodies, and vaccines. This thesis discusses two approaches to targeting two fusion proteins: Env from HIV-1 and S from SARS-CoV-2. The first chapter of this thesis is an introduction to viruses with a specific focus on HIV-1 CD4 mimetic drugs and antibodies against SARS-CoV-2. It discusses the architecture of these viruses and fusion proteins and how small molecules, peptides, and antibodies can target these proteins successfully to treat and prevent disease. In addition, a brief overview is included of the techniques involved in structural biology and how it has informed the study of viruses. For the interested reader, chapter 2 contains a review article that serves as a more in-depth introduction for both viruses as well as how the use of structural biology has informed the study of viral surface proteins and neutralizing antibody responses to them. The subsequent chapters provide a body of work divided into two parts. The first part in chapter 3 involves a study on conformational changes induced in the HIV-1 Env protein by CD4-mimemtic drugs using single particle cryo-EM. The second part encompassing chapters 4 and 5 includes two studies on antibodies isolated from convalescent COVID-19 donors. The former involves classification of antibody responses to the SARS-CoV-2 S receptor-binding domain (RBD). The latter discusses an anti-RBD antibody class that binds to a conserved epitope on the RBD and shows cross-binding and cross-neutralization to other coronaviruses in the sarbecovirus subgenus.</p
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Into the Multiverse: Methods for Studying Developmental Neuroscience
One major challenge in developmental neuroscience research is the sheer number of choices researchers face when addressing even a single research question. Even once data collection is complete, the journey from raw data to interpretation of findings may depend on numerous decisions. To address this issue, this dissertation explores “multiverse” analysis techniques for following many analytical paths at once in the same dataset.
In chapter 1, multiverses are used to examine which analyses of age-related change in amygdala-medial prefrontal cortex circuitry are robust versus sensitive to researcher decisions. Chapter 2 uses multiverse analysis to identify optimal solutions for mitigating breathing-induced artifacts in resting-state functional magnetic resonance imaging data. Chapter 3 uses a variety of model specifications to characterize simultaneous reward learning strategies in youth contingent on both visual task cues and spatial-motor information.
Despite varied approaches and goals, each of the three studies highlight the benefits of conducting multiple parallel analyses for both addressing questions in developmental neuroscience and deepening understanding of the methods used to address them
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