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This chapter introduces the topic of mental health and wellbeing in schools and the overarching theoretical framework for the book. The evolving role of schools from nurturing environments to frontline support for pupils’ mental health and wellbeing is scrutinised in the context of demand, capacity and constraints in a post-pandemic world. Alongside this is a critical consideration of schools’ and education staff’s role, responsibilities and boundaries. The disparate mental health needs of pupils within the school population are discussed, as well as key risk and protective factors to help facilitate timely identification and appropriate support pathways for vulnerable pupils. Key topics and case studies which comprise the remainder of the book are introduced to navigate the reader, and this concludes the chapter.

This study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.

The methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.

Findings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.

The practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.

The originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.

Finite element (FE) analysis can be used for both design and verification of components. In the case of 3D-printed materials, a proper characterization of properties, accounting for anisotropy and raster angles, can help develop efficient material models. This study aims to use compression tests to characterize short carbon-reinforced PA12 made by fused filament fabrication (FFF) and to model its behaviour by the FE method.

In this work, the authors focus on compression tests, using post-processed specimens to overcome external defects introduced by the FFF process. The material’s elastoplastic mechanical behaviour is modelled by an elastic stiffness matrix, Hill’s anisotropic yield criterion and Voce’s isotropic hardening law, considering the stacking sequence of raster angles. A FE analysis is conducted to reproduce the material’s compressive behaviour through the LS-DYNA software.

The proposed model can capture stress values at different deformation levels and peculiar aspects of deformed shapes until the onset of damage mechanisms. Deformation and damage mechanisms are strictly correlated to orientation and raster angle.

The paper aims to contribute to the understanding of 3D-printed material’s behaviour through compression tests on bulk 3D-printed material. The methodology proposed, enriched with an anisotropic damage criterion, could be effectively used for design and verification purposes in the field of 3D-printed components through FE analysis.

This study employs an interpretivist approach to investigate how blockchain technology can transform the remittance industry.

We collected data from blockchain experts with extensive experience, compellingly demonstrating the revolutionary impact of blockchain technology on the remittance sector.

Despite experiencing rapid growth, the global remittance sector still faces challenges such as high costs and lengthy transaction times. However, blockchain technology, known for disrupting emerging industries, offers a decentralized and secure mechanism for containing, storing and managing information. By leveraging rapid block confirmations, blockchain reduces settlement times in remittance transactions, enabling value transfers between parties with just a crypto wallet.

The implications of this study are profound for theorists, as it validates existing theoretical frameworks, identifies specific pain points and explores exciting opportunities presented by blockchain technology. Additionally, we provide a roadmap for future research and innovation, bridging theory and practice and setting the stage for advancement, fostering knowledge, innovation and the successful integration of blockchain technology in the remittance industry.

Analysis highlights a unanimous consensus among experts regarding the transformative potential of blockchain technology in the remittance industry. This consensus serves as a strong impetus for practitioners to swiftly adopt blockchain-based solutions, capitalizing on opportunities such as stable coin issuance, elimination of intermediaries in foreign exchange trading, access to liquidity and the exploration of new business models. Successfully addressing challenges like congestion and limited reach will ensure seamless integration.

This study aims to investigate the synergies between the economic environment and the smart living dimension embedded in the current smart city initiatives, focusing on the localization of female entrepreneurship in contemporary cities. This interaction is under-investigated and controversial as it includes cities' practices enabling users and citizens to develop their potential and build their own lives, affecting entrepreneurial and economic outcomes. Building upon the perspective of the innovation ecosystems, this study focuses on the impact of smart living dimensions and R&D investments on the localization of female entrepreneurial activities.

The study uses a Generalized Method of Moments (GMM) and a panel dataset that considers 30 Italian smart city projects for 12 years to demonstrate the relationship between smart living practices in cities and the localization of female entrepreneurship. The complementary effect of public R&D investment is also included as a driver in the “smart” city transition.

The study found that the advancement of smart living practices in cities drives the localization of female entrepreneurship. The study highlights the empirical results, the interaction over the years and a current overview through choropleth maps. The public R&D investment also affects this relationship.

This study advances the theoretical discussion on (1) female entrepreneurial intentions, (2) smart city advancement (as a context) and (3) smart living dimension (as a driver) and offers valuable insight for governance and policymakers.

This study offers empirical contributions to the preliminary academic debate on enterprise development and smart city trajectories at the intersection between human-based practices and female entrepreneurship.

This study offers empirical contributions to the preliminary academic debate on enterprise development and smart city trajectories at the intersection between human-based practices and female entrepreneurship. The findings provide valuable insights into the localization of female entrepreneurship in the context of smart cities.

This paper aims to unravel the puzzle that the United Kingdom’s high-quality government accounting and fiscal architecture is associated with low-quality outcomes, including poor productivity growth, high public debt, public services which do not meet citizen expectations and historically high levels of taxation. It contributes to public sector accounting research in the fields of fiscal transparency and governance.

This paper uses Miller and Power’s (2013) economization framework and Dunsire’s (1990) concept of collibration to explain why being a global leader in public sector accounting reform and in fiscal and monetary architecture has not protected the UK from weak governance. The intersection of economization’s roles of accounting with modes of government accounting clarifies the puzzle.

Whereas accruals government accounting contributes to fiscal transparency, this is not a sufficient condition for well-judged policy and its effective application. Collibration is the dominant mechanism for mediation in the fiscally centralized UK, but it has failed to deliver stable outcomes, in part because Parliament is limited in its ability to hold back inappropriate behaviour by the Executive. Subjectivization has disrupted adjudication because governments at all levels resist constraints on their behaviour, with unpredictable and often damaging consequences.

This paper provides insights through the combined lens of economization and modes of government accounting, demonstrating the practical value of this conceptualization. Although some causes for unsatisfactory outcomes are specific to the UK, there are cautions for accounting and fiscal reformers in other countries, such as Member States of the European Union.

This paper aims to explore factors contributing to music popularity using machine learning approaches.

A dataset comprising 204,853 songs from Spotify was used for analysis. The popularity of a song was predicted using predictive machine learning models, with the results showing the superiority of the random forest model across key performance metrics.

The analysis identifies crucial genre and audio features influencing music popularity. Additionally, genre specific analysis reveals that the impact of music features on music popularity varies across different genres.

The findings offer valuable insights for music artists, digital marketers and music platform researchers to understand and focus on the most impactful music features that drive the success of digital music, to devise more targeted marketing strategies and tactics based on popularity predictions, and more effectively capitalize on popular songs in this digital streaming age.

While previous research has explored different factors that may contribute to the popularity of music, this study makes a pioneering effort as the first to consider the intricate interplay between genre and audio features in predicting digital music popularity.