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The Intellect handbook of popular music methodologies
No full textThe Handbook of Popular Music Methodologies provides a comprehensive overview of methodological approaches with regards popular music studies. Alongside contributions from key thinkers already established in popular music studies, the strength of the collection lies in its inclusion of many new and emerging writers in the field. Therefore, the collection incorporates a wide range of practitioners, pedagogues and academics from a extensive range of disciplines, and thus drawing from a diversity of methodological approaches. These include those that are perhaps more established, such as semiotics, ethnography and psychology, alongside exciting new approaches within popular music. These include, eco-musicology, popular music and religion, intersectionality, archeology and cybernetics. A particular section also looks at the importance of popular music studies within the ‘virtual,’ including chapters on composition and artificial intelligence, livestreaming and esports. Although previous books have provided an overall of concepts studied within popular music studies, this will be the first comprehensive Handbook of popular music methodologies
Architecting digital twins for intelligent transportation systems
Get PDFModern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data.
To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency
The role of social signaling and ethnic norms in charitable giving: a field experiment in Vietnam
No full textIn this field experiment involving ethnic and income groups in Vietnam, we explore the role of social signaling—a construct encompassing both status-seeking and public recognition—in charitable giving. We find that individuals from the Hoa (Chinese) ethnicity and those in higher income brackets are more prone to engage in social signaling by donating more. Conversely, the Khmer, influenced by their ethnic norms, are less likely to use donations as a form of social signaling. Our findings align with a multifaceted theoretical model that integrates social signaling and ethnic norms to explain the complexities of charitable giving behavior. This study not only enriches our understanding of why people donate but also underscores the nuanced interplay of social signaling and cultural norms
Enhancing employability and engagement in a student-centred learning environment: insights from the MDX internship scheme
Get PDFThe higher education sector faces significant challenges, including the need to enhance student employability, engagement, and financial accessibility. Universities must equip graduates with both academic knowledge and practical skills while addressing financial barriers that hinder many students’ progress. The MDX Internship Scheme, launched at Middlesex University in January 2024, responds to these challenges by offering paid, discipline-specific internships that integrate practical work experience with academic learning. The scheme enhances students’ career readiness by enabling them to apply theoretical knowledge in real-world settings, develop essential skills such as teamwork and problem-solving, and build confidence through project achievements. Moreover, the internships alleviate financial burdens and foster a sense of belonging.
This paper showcases the experiences of engineering undergraduates, postgraduates, and doctoral candidates, who participated in the MDX Internship scheme and undertook diverse projects spanning academic research, industry placements, and collaborations with local authorities, deepening their understanding of engineering and transferable skills. Local businesses also benefited from the scheme, gaining access to innovative student talent that positively influenced workplace dynamics and project outcomes, while building long-term partnerships with the university. The MDX Internship Scheme serves as a model for enhancing employability, engagement, and financial support in higher education
Continuity and change in national HRM – an overview and future research agenda
No full textContext and contextual changes over time are important in the study of organisations, their management generally and human resource management (HRM) in particular. Prominent theoretical schools such as various forms of institutionalist and contingency thinking as well as calls for considering context in organisational behaviour testify to this importance. In a similar manner, the issue of time is clearly acknowledged and, recently, has received some additional attention. In editing this special issue (SI), we emphasise that context and time are not only each separately important but also intersect and must be considered simultaneously
Parameter tuning of the firefly algorithm by three tuning methods: Standard Monte Carlo, quasi-Monte Carlo and latin hypercube sampling methods
No full textThere are many different nature-inspired algorithms in the literature, and almost all such algorithms have algorithm-dependent parameters that need to be tuned. The proper setting and parameter tuning should be carried out to maximize the performance of the algorithm under consideration. This work is the extension of the recent work on parameter tuning by Joy et al. (2024) presented at the International Conference on Computational Science (ICCS 2024), and the Firefly Algorithm (FA) is tuned using three different methods: the Monte Carlo method, the Quasi-Monte Carlo method and the Latin Hypercube Sampling. The FA with the tuned parameters is then used to solve a set of six different optimization problems, and the possible effect of parameter setting on the quality of the optimal solutions is analyzed. Rigorous statistical hypothesis tests have been carried out, including Student's t-tests, F-tests, non-parametric Friedman tests and ANOVA. Results show that the performance of the FA is not influenced by the tuning methods used. In addition, the tuned parameter values are largely independent of the tuning methods used. This indicates that the FA can be flexible and equally effective in solving optimization problems, and any of the three tuning methods can be used to tune its parameters effectively
The effects of a 10-week strength and conditioning intervention on physical capacity and golf shot performance in university male and female golfers
No full textThe aim of the present study was to undertake a 10-week strength and conditioning (S&C) training intervention and determine the effects on golf shot data and physical capacity, in both high-level male and female university golfers. A total of 11 golfers (males = 6; females = 5) undertook a comprehensive testing battery pre and post-intervention, consisting of: golf shot data (with a driver), an isometric mid-thigh pull, isometric bench press, countermovement jump, seated medicine ball throw, and thoracic spine mobility. For the 10-week intervention, all players undertook 6-weeks of strength training twice per week, followed by 4-weeks of power training twice per week. No statistically significant differences were evident in the male group; however, large improvements (as defined by Hedges g effect size analysis) were observed for force at 100 ms in the isometric bench press (g = 1.13), carry distance (g = 0.98), and ball speed (g = 0.82). In the female group, significant large improvements were evident in all isometric bench press metrics – force at 100 ms (g = 3.79; p < 0.05), force at 200 ms (g = 3.04; p < 0.05), and peak force (g = 1.54; p < 0.05), in addition to non-significant large increases in carry distance (g = 0.84). In summary, this intervention showed that large improvements in golf shot data can be made from undertaking strength and power training twice a week for 10-weeks, for both male and female players. Furthermore, and aligned with previous research in female golf, the importance of maximal and rapid force production in the upper body appears to be of particular importance for female golfers
Suicide prevention measures at high-risk locations: a goal-directed motivation perspective
Get PDFUnderstanding the effectiveness of suicide prevention measures for high-risk locations can often be challenging as many rely, at least to some degree, on psychological processes (e.g., engaging with help-seeking behaviours). Establishing how these measures may influence decision-making during a suicide attempt could be helpful for understanding how and when they may be most effective at preventing deaths. In the present work, we consider how suicide prevention measures may influence “goal pur-suit” as it unfolds. Drawing on findings from across the suicide prevention literature, we apply the descriptive framework outlined in GOAL Architecture to consider how different measures may shape perceptions of “distance”, “time”, and “rate of progress” and, in turn, could influence levels of motivational drive associated with specific acts (e.g., “ac-cessing means for suicide”). This is discussed in relation to real-time decisions around accessing means for suicide, avoiding intervention by a third-party, and engaging in help-seeking behaviours. As well as the psychological processes which could encourage or prevent an individual from disengaging from a suicide attempt, we also consider potential risks and influence of person-level factors
Self-compassion around the world: measurement invariance of the short form of the self-compassion scale (SCS-SF) across 65 nations, 40 languages, gender identities, and age groups
Get PDFObjectives
The 12-item Self-Compassion Scale–Short Form (SCS–SF) is a widely used instrument for the assessment of self-compassion. To date, there have been few examinations of this instrument’s psychometric properties, particularly across nations and languages. Therefore, we used data from the Body Image in Nature Survey (BINS) to assess measurement invariance of the SCS–SF across nations, languages, gender identities, and age groups.
Methods
Participants (N = 56,968) from 65 nations completed the SCS–SF in 40 languages. Using these data, we tested various hypothesised models of the SCS–SF in the total sample and, using multi-group confirmatory factor analysis, tested for invariance of the optimal model across national groups, languages, gender identities, and age groups.
Results
In the total dataset, we found that an 11-item, 2-factor model (i.e., SCS-11) provided best fit to the data, with the two factors tapping distinct constructs of compassionate and uncompassionate self-responding. The SCS-11 was found to be partially scalar invariant across national groups and languages, and fully scalar invariant across gender identities and age groups. There was wide variation in latent means for the two factors, particularly across national groups and languages. Further analyses showed negligible associations between the two factors and sociodemographic variables, including marital status, financial security, and urbanicity.
Conclusions
Our results suggest that it may be possible to derive a stable 2-factor model of the SCS–SF for use in cross-cultural research, but also highlight the likelihood of cross-national and cross-linguistic variations in the way that self-compassion is understood
Navigating the N-Person Prisoner's Dilemma: from the tragic valley to the collaborative hill
No full textThe N-Person Iterated Prisoners’ Dilemma (N-IPD) is an excellent environment to explore collaboration. This paper shows that the voting mechanism is crucial in determining whether sets of agents collaborate, or defect. When each agent can vote against each other agent individually, the agents become cooperative much more easily, ascending the Collaborative Hill. When the agents have only one vote each round, they tend to defect, descending into the Tragic Valley. This is shown with static decision policies, and with policies that learn using reinforcement learning. Fortunately, when agents retain enough history, they can become collaborative even with one vote each round. This voting policy difference is due to the shape of the reward space.The research community has a great deal of knowledge about neural function and brain topology. This knowledge is by no means complete, but much of it is quite solid and is generally accepted as true. We try to take advantage of this knowledge as the basis of our models. For example, it is known that the brain is made up of neurons and these neurons connect to other neurons at places called synapses. (They may connect at other places, but most connections are synaptic.) When a pre-synaptic neuron fires, it sends activation (or inhibition) across the synapse, to the post-synaptic neuron. If the post-synaptic neuron collects enough energy it will fire