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Terrors of the Night: Essays on Art, Myth and Dreams
No full textThis volume presents essays on the history of dreams and nightmares, in particular focusing on how these have been visualised; and how and why these visualizations change. Using the concept of dream-culture, the book explores dreams of fear and joy throughout history, examining their context in myth and lived experience through their imagery and their significance in art, literature, magic and religion.Chapters deal with dreams among the Inuit, the night-riders of Renaissance Europe, the figure of the mermaid-siren, battle madness and shape-shifting, twentieth-century outsider artists and the invention of the modern nightmare, charms and curses from antiquity to the present day. The book draws on a wide range of perspectives from comparative mythology, anthropology, art history and critical theory, to show how people in different times and places have harnessed their dream-culture and used it to address challenges and threats in everyday life. Dream-culture in this sense is also seen as a creative arena, in close conjunction with the paradigms and media of visual art, folk culture and belief in the supernatural
Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms
No full textMachine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their complex interactions, it is common to use optimization techniques to find settings that lead to high predictive performance. However, insights into efficiently exploring this vast space of configurations and dealing with the trade-off between predictive and runtime performance remain challenging. Furthermore, there are cases where the default hyperparameters fit the suitable configuration. Additionally, for many reasons, including model validation and attendance to new legislation, there is an increasing interest in interpretable models, such as those created by the decision tree (DT) induction algorithms. This paper provides a comprehensive approach for investigating the effects of hyperparameter tuning for the two DT induction algorithms most often used, CART and C4.5. DT induction algorithms present high predictive performance and interpretable classification models, though many hyperparameters need to be adjusted. Experiments were carried out with different tuning strategies to induce models and to evaluate hyperparameters’ relevance using 94 classification datasets from OpenML. The experimental results point out that different hyperparameter profiles for the tuning of each algorithm provide statistically significant improvements in most of the datasets for CART, but only in one-third for C4.5. Although different algorithms may present different tuning scenarios, the tuning techniques generally required few evaluations to find accurate solutions. Furthermore, the best technique for all the algorithms was the Irace. Finally, we found out that tuning a specific small subset of hyperparameters is a good alternative for achieving optimal predictive performance
Application of machine learning in predicting frailty syndrome in patients with heart failure
Get PDFPrevention and diagnosis of frailty syndrome (FS) in patients with heart failure (HF) require innovative systems to help medical personnel tailor and optimize their treatment and care. Traditional methods of diagnosing FS in patients could be more satisfactory. Healthcare personnel in clinical settings use a combination of tests and self-reporting to diagnose patients and those at risk of frailty, which is time-consuming and costly. Modern medicine uses artificial intelligence (AI) to study the physical and psychosocial domains of frailty in cardiac patients with HF. This paper aims to present the potential of using the AI approach, emphasizing machine learning (ML) in predicting frailty in patients with HF. Our team reviewed the literature on ML applications for FS and reviewed frailty measurements applied to modern clinical practice. Our approach analysis resulted in recommendations of ML algorithms for predicting frailty in patients. We also present the exemplary application of ML for FS in patients with HF based on the Tilburg Frailty Indicator (TFI) questionnaire, taking into account psychosocial variables
Introduction: Between the Field and the Gallery: Exploring Anthropological Knowledge in South Asia
Get PDFThis special section brings together the work of historians, anthropologists and museologists, exploring how anthropological and sociological knowledge has been produced, consumed and reproduced in India. In particular, the special section is interested in analysing how these academic disciplines consolidated themselves as sciences informing the way a newly independent nation-state was to define its future, present and past in social and cultural terms. Anthropology and sociology were seen as tools to understand the diverse ethnic, religious and cultural background of India. Scholars used these disciplines to define what aspects of society were foreign or indigenous; what cultures, religions and languages were to be preserved; and what aspects of society were to be eliminated or integrated in order to achieve ‘progress’. Thus, anthropology and sociology shaped and continued to shape how we see India as a country and society. To understand how this process took place, the articles forming this special section are organised under three main categories: genealogies, methods and museums. The section on genealogies explores the way the first generation of Indian anthropologists and sociologists simultaneously challenge racial hierarchies coming from Europe while accepting caste and racial differences in India. The ‘methods’ sections looks at the trend of action anthropology in this case through social science approaches to ‘marginal’ citizens, whose cultures were explored as part of a larger citizenship complex stressing modernity, assimilation and secularism. The museum section contextualises a wide variety of national, state and house museums within the post-independence tribal integration policies and explores indigenous critiques that decolonise essentialising narratives of the state and nation
Mental health in Ukraine in 2023
Get PDFBackgroundVery little is known about the mental health of the adult population of Ukraine following Russia’s full-scale invasion in February 2022. In this study, we estimated the prevalence of seven mental health disorders, the proportion of adults screening positive for any disorder, and the sociodemographic factors associated with meeting requirements for each and any disorder.MethodsA non-probability quota sample (N = 2,050) of adults living in Ukraine in September 2023 was collected online. Participants completed self-report questionnaires of the seven mental health disorders. Logistic regression was used to determine the predictors of the different disorders.ResultsPrevalence estimates ranged from 1.5% (cannabis use disorder) to 15.2% (generalized anxiety disorder), and 36.3% screened positive for any of the seven disorders. Females were significantly more likely than males (39.0% vs. 33.8%) to screen positive for any disorder. Disruption to life due to Russia’s 2014 invasion of Ukraine, greater financial worries, and having fewer positive childhood experiences were consistent risk factors for different mental health disorders and for any or multiple disorders.ConclusionOur findings show that approximately one in three adults living in Ukraine report problems consistent with meeting diagnostic requirements for a mental health disorder 18 months after Russia’s full-scale invasion. Ukraine’s mental healthcare system has been severely compromised by the loss of infrastructure and human capital due to the war. These findings may help to identify those most vulnerable so that limited resources can be used most effectively
PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing
Get PDFIn the field of remote sensing image interpretation, automatically extracting water body information from high-resolution images is a key task. However, facing the complex multi-scale features in high-resolution remote sensing images, traditional methods and basic deep convolutional neural networks are difficult to effectively capture the global spatial relationship of the target objects, resulting in incomplete, rough shape and blurred edges of the extracted water body information. Meanwhile, massive image data processing usually leads to computational resource overload and inefficiency. Fortunately, the local data processing capability of edge computing combined with the powerful computational resources of cloud centres can provide timely and efficient computation and storage for high-resolution remote sensing image segmentation. In this regard, this paper proposes PMNet, a lightweight deep learning network for edge-cloud collaboration, which utilises a pipelined multi-step aggregation method to capture image information at different scales and understand the relationships between remote pixels through horizontal and vertical spatial dimensions. Also, it adopts a combination of multiple decoding branches in the decoding stage instead of the traditional single decoding branch. The accuracy of the results is improved while reducing the consumption of system resources. The model obtained F1-score of 90.22 and 88.57 on Landsat-8 and GID remote sensing image datasets with low model complexity, which is better than other semantic segmentation models, highlighting the potential of mobile edge computing in processing massive high-resolution remote sensing image data
On the Integrity of Large-Scale Direct-Drive Wind Turbine Electrical Generator Structures: An Integrated Design Methodology for Optimisation, Considering Thermal Loads and Novel Techniques
Get PDFWith the rapid expansion of offshore wind capacity worldwide, minimising operation and maintenance requirements is pivotal. Regarded as a low-maintenance alternative to conventional drivetrain systems, direct-drive generators are increasingly commonplace for wind turbines in hard-to-service areas. To facilitate higher torque requirements consequent to low-speed operation, these machines are bulky, greatly increasing nacelle size and mass over their counterparts. This paper therefore details the structural optimisation of the International Energy Agency 15 MW Reference Wind Turbine rotor through iterative Parameter and Topology Optimisation and the inclusion of additional structural members, with consideration to its mechanical, modal, and thermal performances. With temperature found to have a significant impact on the structural integrity of multi-megawatt direct-drive machines, a Computational Fluid Dynamics analysis was carried out to map the temperature of the structure during operation and inform a consequent Finite Element Method analysis. This process, novel to this paper, found that topologically optimised structures outperform parametrically optimised structures thermally and that integrated heatsinks can be employed to further reduce deformation. Lastly, generative design techniques were used to further optimise the structure, reducing its mass, deformation, and maximum stress and expanding its operating envelope. This study reaches several key conclusions, demonstrating that significant mass reductions are achievable through the removal of cylinder wall geometry areas as well as through the implementation of structural supports and iterative parametric and topology optimisation techniques. Through the flexibility it grants, generative design was found to be a powerful tool, delivering further improvements to an already efficient, yet complex design. Heatsinks were found to lower generator structural temperatures, which may yield lower active cooling requirements whilst providing structural support. Lastly, the link between the increased mass and the increased financial and environmental impact of the rotor was confirmed
Creative Informatics E11 Final Report
Get PDFThis report reflects on the investment of Creative Informatics (CI) at Edinburgh Napier University in the physical infrastructure of a lab, E11 to support the use of emerging new creative technologies by creatives. The report discusses the types of equipment invested in and how this equipment supported R&D that was used by both creatives and academic practitioners and considers how this type of low threshold borrowing might be supported in the future to support the development and uptake of CreaTech in the creative industries. The report reflects on the different types of R&D activities which took place in the E11 space for exploration, experiencing and experimentation of creative tech by creative businesses and researchers. Additionally, the studio was used to support over twenty individual creative and academic R&D projects. A programme of E11 outreach events (Studios and Friday Forums), hosted in-person and online during the global pandemic, reached with 758 participants
Fintech's Influence on Green Credit Provision: Empirical Evidence from China’s Listed Banking Sector
Get PDFWe explore the impact of financial technology (fintech) advancements on green credit provision, investigating publicly traded banks in China from 2007 to 2022. We particularly focus on credit modelling innovation, examining the non-linear dynamics between fintech evolution and green credit distribution. Results reveal a positive U-shaped correlation. Initial stages of fintech are associated with increased green credit risk, negatively affecting the volume of green credit. However, more established fintech infrastructures significantly enhance green credit volumes by improving resource allocation and credit risk assessment. Utilizing a multiple linear regression approach, we highlight the transformative nature of fintech in advancing sustainable banking practices, particularly through innovations in credit modeling that enhance green credit risk management and resource allocation efficiency
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