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Electrocatalytic OER behavior of Bi-Fe-O system: An understanding from the perspective of the presence of oxygen vacancies
Get PDFThis study aims to understand and correlate the role of the nature and relative concentration of the oxygen vacancies with the trend observed in OER with the Bi Fe-O system. To understand this, we first investigated the system of oxides using X-ray Photoelectron Spectroscopy (XPS) and Electron Paramagnetic Resonance (EPR), which revealed the presence of oxygen vacancies in the system. Density functional theory (DFT) was employed to investigate the relative concentration of these vacancies by calculating their formation energies. Positron Annihilation Lifetime Spectroscopy (PALS) was carried out to understand the nature of these oxygen vacancies. We observed that the presence of a higher concentration of monovacancies created by the absence of oxygen from the structure of Bi2Fe4O9 was majorly responsible for the high performance of the oxide towards OER compared to the other oxides viz—BiFeO3 and Bi25FeO40 of the Bi-Fe-O system
The Psychology of Creative Performance and Expertise
No full textThis much-needed book introduces readers to the related fields of expertise, creativity, and performance, exploring our understanding of the factors contributing to greatness in creative domains.
Bringing together research from the fields of creativity and expertise, it provides fresh insights for newcomers and seasoned scholars alike with its approachable guide to the multidimensional complexities of expertise development. It transcends traditionally studied fields such as chess, sports, and music, instead exploring the intersection of expertise with creativity and the performing arts. Dedicated applied chapters cover eight fields, including mind-games, music, dance, creative writing, acting, art, and STEM. The book also examines the facilitators of creative performance, including aesthetic sensitivity, creativity, and mental imagery, as well as the obstacles to performance, such as burnout, procrastination, and gender-related challenges. The book concludes by engaging with pressing issues facing expertise, including the impact of AI. Student-friendly pedagogy is featured throughout, including 'Spotlight on...', 'Check it out...', and 'Consider this...' boxes to position material within context and engage students' learning.
Whether revealing how an actor brings their part to life, how writers conjure up their storylines and vibrant characters, or what lies behind scientific invention, The Psychology of Creative Performance and Expertise offers a fascinating insight into the multifaceted journey towards achieving creative excellence. This is a valuable resource for final-year undergraduate and postgraduate students, and scholars across a range of disciplines, including expertise or skill acquisition, the psychology of performance, and creativity
De Facto Precedent at the Court of Justice of the European Union
Get PDFThis paper seeks to demonstrate that although there is no official doctrine of precedent in judgments of the Court of Justice of the European Union (CJEU), the research affirms that there is a de facto system of precedent. This means that whilst, de jure, there is no official precedent status of the case law of the CJEU, the Court does give precedential value to its own case law through interpretive practices to ensure the uniform application of law and legal certainty throughout the Member States of the European Union (EU). When one looks at the elements of precedent it is apparent that this goes beyond its legal value (i.e. authority or bindingness) or conscious jurisprudential choice – language also plays a role. This article will examine discussions and models of precedent in common law and civil law legal systems in both theory and practice, before going on to examine the theories and practices of precedent at the CJEU
Growing a Small Firm in an Industry Dependent on a Constrained Natural Resource
Get PDFThe research focuses on the challenge of growing a small firm while the resources needed (e.g., fish) are declining and, hence, heavily regulated. The seafood industry is still growing, and fish as a protein is becoming more popular due to its positive effect on health, representing a growth opportunity. However, small fishing firms are exiting the market, performing erratically or simply stalling, which leads to the central question of this research: How can small firms grow in an industry where the natural resource is in decline? The research explores and evaluates factors shaping small firms' growth in Northern Norway's fishing industry.
This research adopts a critical realism paradigm. Unlike previous research on small firm growth, which has been dominated by positivism, this thesis follows a different approach, pivoting around the challenge to grow a small firm and questioning underlying assumptions of existing theories. The philosophy adopted in this research enabled combining the exploratory nature of the question and the explanatory aspects of identifying factors leading to firm growth, applying a mixed method abductive approach. The context of the research is the fishing industry in Northern Norway, and the data include secondary sources collected from two central databases, and qualitative primary data from eight semi-structured interviews and 124 professional trade publication articles.
The outcome of this thesis shows apparent differences in firms' performance, all exposed to similar macroeconomics and natural resource constraints, and that growth rates of small and large firms differ. The data also indicate that fish stock variations impact firm growth and that current mechanisms of adjusting the price per kilo of fish based on allocated quotas result in fluctuating and unpredicted revenues year on year, negatively impacting the growth of small firms. The thesis contributes to knowledge with a proposed business model containing four groups of variables sorted based on their impact on firm growth. The first group of variables is location, regulations, and risk management capabilities, predominantly growth inhibitors. The second group is principally growth enablers and includes collaboration & competition, efficiency & reliability, and revenue mechanisms. The third group is business acumen, knowledge, and personal preferences. It plays the function of dials to move the role of the first two groups of variables between inhibitors and enablers in a continuum. Finally, the fourth group includes beyond the status quo and the boat and acts as an accelerator of growth, pushing the overall dominant position of the firm from “as is” to “growth”. The thesis also proposes a mathematical representation of the model. Small firms seeking to grow could adopt this model to adjust their businesses, representing the contribution to the practical management of this research
Neo-liberalism, Human Capital Theory and the Right to Education: Economic Interpretation of the Purpose of Education
Get PDFWith the end of WW II, a new world order emerged that recognised the significance of human rights as part of the remedial measures to institute global peace. This is recognised in Articles 1(3), 13(1)(b) and 55(c) of the 1945 United Nations Charter. Thereafter, the human rights ideals recognised by the UN Charter were codified into the Universal Declaration of Human Rights (UDHR) 1948 (Fait, 2015: 26). Despite not having a binding force, the UDHR became a standard-setting instrument covering all generations of human rights including the right to education. Later, two distinct treaties – i.e., the International Covenant on Civil and Political Rights (ICCPR) 1966 and the International Covenant on Economic, Social and Cultural Rights (ICESCR) 1966 were adopted as a follow-up to the UDHR. Articles 13 and 14 of the ICESCR made more expansive provisions on the right to education than Article 26 of the UDHR. However, the adoption of policies driven by neoliberal ideals and associated neo-classical economic principles in the delivery of education has brought education under market forces, encapsulating it with an economic purpose. This makes education central to the realisation of the neoliberal ideology as schools focus on teaching technical skills and knowledge necessary for the achievement of the economic purposes of education. This paper argues that while the economic purpose of education which is in line with neoliberal and associated neo-classical economic principles is germane for states’ economic development, a holistic approach is consistent with the human rights purpose of education
ENAS-B: Combining ENAS with Bayesian Optimisation for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification from Ultrasound Images
Get PDFEfficient Neural Architecture Search (ENAS) is a recent development in searching for optimal cell structures for Convolutional Neural Network (CNN) design. It has been successfully used in various applications including ultrasound image classification for breast lesions. However, the existing ENAS approach only optimises cell structures rather than the whole CNN architecture nor its trainable hyperparameters. This paper presents a novel framework for automatic design of CNN architectures by combining strengths of ENAS and Bayesian Optimisation in two folds. Firstly, we use ENAS to search for optimal normal and reduction cells.
Secondly, with the optimal cells and a suitable hyperparameter search space, we adopt Bayesian Optimisation to find the optimal depth of the network and optimal configuration of the trainable hyperparameters. To test the validity of the proposed framework, a dataset of 1,522 breast lesion ultrasound images is used for the searching and modelling. We then evaluate the robustness of the proposed approach by testing the optimized CNN model on three external datasets consisting of 727 benign and 506 malignant lesion images. We further compare the CNN
model with the default ENAS-based CNN model, and then with CNN models based on the state-of-the-art architectures. The results (error rate of no more than 20.6% on internal tests and 17.3% on average of external tests) showed that the proposed framework generates robust and light CNN models
‘In fair Verona where we lay our scene’: The Inspiration and Treatment of Italy In Shakespeare’s Plays
Get PDFThis paper examines the role of fifteenth- and sixteenth- century Italian location, society, and individuals on the works of William Shakespeare. By examining the Italianate works of the playwright, I strive to understand what connected Shakespeare to his Italian sources, locations, and literature.
The thesis is interested in how as much as why Shakespeare was influenced by Italy. By incorporating detailed analysis of the Italianate and Roman plays, connections to Italian literature, and examining clues left by Shakespeare’s contemporaries, this study demonstrates the difficulty in attempting to uncover a certain answer to the question of how Shakespeare was able to access different aspects of Italy including its writers, literature, and geographical layout. This paper will consider Shakespeare’s education, social connections, and personal experiences when discussing the connection between the playwright and Italy. It argues that Shakespeare would have had enough education to aid him in reading certain pieces of Italian literature without a translation, helping him to recall information from the Roman Empire he would have learnt, and Latin literature on famous individuals he would have studied. It will also look at the reception that Italy has given Shakespeare; examining how famous cities have embraced, appropriated, and adapted their location to fit Shakespeare’s version. Understanding the link between one of England’s most famous playwright and Italy is important when thinking about what inspired the tales that have become ingrained within different elements of human society all over the world
Image Data Preparation For CNN-based Breast Ultrasound Lesion Diagnostic with Reduced Overfitting
Get PDFThis thesis aims to contribute to efforts of leveraging deep learning (DL) techniques, specifically convolutional neural networks (CNNs), for improved diagnostics of breast lesions in ultrasound (US) images with reduced overfitting manifested by the inability to generalise models to unseen data. Our investigations focus on data preparation factors that influence the performance of CNN models for analysing Breast US (BUS) tumour images. These factors include CNN stipulated fixed input image size, adequate US image quality, and availability of a sufficiently large dataset of adequately labelled samples with good class-diversity for training. Current approaches to deal with these factors are focused on image resizing, relying on unstandardized manual quality assessment by radiology experts, and image augmentation. Most of these solutions rely heavily on knowledge of the natural image domain, which differs from US images. The sizes of US tumour region of interest (RoI) are influenced by the adopted cropping/segmentation procedure and vary significantly with a huge range on both sides of the strictly required input image size for most CNN models. Resizing the many tiny RoIs by several factors seriously impacts their quality. Existing augmentation schemes are designed to enlarge training sets and increase diversity, but the learnt feature patterns by pre-trained CNN models are more relevant to natural images. We implemented the bicubic image resizing (BiCubic) method and a Compressed Sensing Super Resolution (CSSR) based image resizing known for superior quality resizing methods in terms of human perception. Our expert radiologist testified that CSSR resized images are of better quality from the clinical point of view. We tested the performance of several pre-trained CNN models trained in fine-tuning mode on a database of BUS recorded and labelled in one clinical centre, whose RoI images were resized by both methods. All models achieved High-to-Excellent diagnostic accuracy, but little or no improvements were noted with the CSSR resizing scheme. No RoI segmentation was adopted, but optimal cropping of RoI was developed from a set of radiologists’ marked lesion border points. We introduced the Convex Hull (CH) lesion border RoI that efficiently minimizes the exclusion of lesion pixels and is easy to expand. We tested the performance of a few pre-trained CNN models and 2 Handcrafted (HC) schemes with various RoI cropping scenarios, including the tumour polygonal shape. We expanded CH at different rates, each with 2 padding schemes for the area between the surrounding rectangular box and the tumour polygon area: zero padding and tissue padding. While tissue padding of several expanded CH rates had improved performance, zero padding of these schemes was marginally lower. Hence, the inclusion of some external tissue surrounding the lesion border shows promise for enhancing model performance. However, for both padding scenarios, the trained models have very low generalisation when tested on two unseen external datasets, confirming the problem of overfitting when the training dataset is not large and diverse enough. Training the same CNN models with the larger Modelling dataset, compiled by including BUS images from 4 other clinical centres, didn’t improve their validation performance but significantly improved their generalisation to the two unseen datasets. This improvement reflects that the expansion created a more diverse sample of the population resulting in reduced overfitting. For the challenge of US image quality assessment (IQA), we uncovered the inadequacy of existing IQA metrics defined for natural images. We developed a simple Multi Characteristic Quality Feature Vector (MCIQ) that captures the spatial distribution of individual IQA metrics. MCIQ have shown good tumour class dependency and a high ability to distinguish different image modalities and datasets. An innovative version of MCIQ, extracted from image convolution with only 6 well conditioned 5×5 Hadamard filters, successfully aligned with our expert radiologist quality labelling of an extremely small set of US images. Finally, to address the scarcity of BUS images beyond recording a larger training dataset, we investigated several existing conventional image augmentation schemes, including Singular Value Decomposition (SVD), besides our innovative Hadamard filters convolution. All these schemes improved the model’s ability to generalize to the two unseen datasets but with varied levels of improvement. However, these schemes are not specific to US images, so it is difficult to determine which causes of overfitting these schemes help mitigate. For that, we developed the Tumour Margin Appending (TMA) strategy that combines several locally optimal cropping ratios to enlarge the training dataset aiming to alleviate the lack of generalization due to variation in RoI cropping practice. It successfully mitigated the lack of generalization to unseen datasets for this cause and removed the need to test with many unseen datasets
Exploring the Motivation of the United Kingdom’s Domestic Extremist Informants
Get PDFABSTRACT
Understanding a potential informant’s motivation can lay the foundation for managing the risks and opportunities associated with the informant-handler relationship and operational deployments. The present research explored the self-disclosed and handler-assessed motivations of U.K. informants authorized to report against domestic extremists. Informants reported being motivated overwhelmingly by both ideological and financial considerations. Those reporting on right-wing domestic extremism primarily reported for financial reasons, while those reporting on left-wing extremism did so primarily for ideological reasons. The findings also revealed that motivation is neither one dimensional nor unchangeable, with most informants declaring financial and ideological reasons for informing. Handlers were accurate at identifying informants’ primary motivation, with a minority of the handler assessments revealing a perceived change after a six-month period. By designing recruitment approaches around ideological and financial motivational hooks, law enforcement and intelligence agencies may increase the probability of recruitment success, as well as enhance both the effectiveness and longevity of their informant-handler relationship
Helping in Times of Crisis: Examining the Social Identity and Wellbeing Impacts of Volunteering During COVID-19
Get PDFCOVID-19 produced the largest mass mobilisation of collective helping in a generation. Currently, the impact of this voluntary activity is not well understood, particularly for specific groups of volunteers (e.g., new vs existing), and for different amounts of voluntary activity. Drawing on social psychological work on collective helping, and work from the Social Identity Approach to Health, we seek to address this gap through an analysis of survey data from 1001 adults living in the South of England (333 men; 646 women; Age range = 16–85) during the first UK lockdown. Measures included time spent volunteering pre/post
COVID, community identification, subjective wellbeing, and volunteering intentions. Those who volunteered during COVID-19 reported higher levels of community identification than
those who did not. However, subjective wellbeing benefits were only found for those volunteers who maintained the same level (in terms of time) volunteering pre-and-post the
COVID lockdown. New volunteers showed significantly lower levels of wellbeing where they were undertaking 5 or more hours of volunteering a week. Our findings provide unique
insight into the variable relationship with wellbeing for different groups of volunteers, as well as how the experiences and functioning of 'crisis’ volunteering is different to volunteering during ‘normal’ times