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Enhanced Mechanical Properties of Single Carbon Fibres via Electromagnetic Treatment: A Comparative Analysis
No full textThis study investigates the effect of Electromagnetic Field Treatment (EMFT) on the mechanical properties of polyacrylonitrile (PAN)-based single carbon fibres, which are critical materials in high-performance composites widely utilised in the aerospace and automotive sectors due to their superior strength and stiffness. Carbon fibres were subjected to controlled electromagnetic field exposure, with both treated and untreated fibres rigorously evaluated through tensile testing. The treated fibres exhibited a notable 6.12% increase in yield strength, along with a substantial improvement in tensile modulus compared to the control samples. Scanning Electron Microscopy (SEM) analysis revealed a smoother surface morphology in the treated fibres, potentially contributing to enhanced flexibility and a reduction in microscale defects. These findings suggest that EMFT may serve as an effective technique for optimising the mechanical performance of carbon fibres, thereby enhancing their suitability for advanced applications
Happiness and Positive Psychology
No full textHappiness and Positive Psychologyis essential reading for academic professionals in Positive Psychology seeking theoretical insights and for students in Positive Psychology programs looking for foundational knowledge and practical insights
Deep learning approach for stock closing price prediction A hybrid approach using RNN–LSTM architecture
No full textAccurate stock price forecasting remains a challenging yet crucial task in the financial industry due to the non-linear relationships, noisy, and time-dependent nature of the market data. This study presents a deep learning approach known as long-short-term memory (LSTM) for predicting the closing prices of stock using historical data. The model is designed to capture the complex temporal dependencies inherent in stock market sequences, addressing the limitations of traditional statistical models such as ARIMA and linear regression. Using key key characteristics such as past closing prices, the LSTM model achieved high predictive performance with a Mean Squared Error (MSE) of 0.00036, a mean absolute error (MAE) of 0.0096, and a coefficient of determination (R²) of 0.9941, indicating strong generalization and accuracy. The results demonstrate the effectiveness of LSTM architectures in time series forecasting for financial applications. This research contributes to the development of robust and automated decision support tools for investors and sets a performance benchmark for future deep learning models in stock market prediction
Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model
No full textThe creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item-item collaborative filtering. Presently, item recommendation is based completely on ratings like 1-5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users' reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with 79% accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of 21%, precision at 79%, recall at 80% and F1-Score at 79%. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy
From Co-Creation to Value Actualization: A Service-Ecosystem Theory of Transformation in Platform-Mediated Experiential Contexts Introducing the CORE Model as a Mid-Range Theory of Value Actualization
No full textExperiential industries face a growing disconnect: while service-dominant logic (SDL) establishes value as co-created through resource integration (Vargo & Lusch, 2004, 2016), an increasing share of consumers, particularly younger cohorts, seek durable, transformational outcomes as the return on investment in premium experiences (Anderson & Ostrom, 2015; Zimbatu & Russell-Bennett, 2025). We introduce the CORE model (Content, Outlet, Relation, Effect) as a mid-range theory of value actualization. While SDL, TSR, and CCT each address components of transformation, none specifies the institutionalized, relational micro-foundations through which narrative-based co-creation becomes durable value actualization. CORE introduces a previously unarticulated causal sequence linking narrative scaffolding, access orchestration, and participatory institutionalization to measurable transformation. We define value actualization as the institutionalized realization of experiential potential into durable identity, behavioral, or community change. We propose that Relation mediates the Content–Effect link, while Outlet configuration—the platform-mediated orchestration of access—moderates this mediation. This mechanism is under-specified in, but complementary to, TSR and consumer culture theory (CCT; Arnould & Thompson, 2005). We differentiate CORE from competing frameworks and outline a multi-method research agenda
Leisure and welfare in Britain from the Industrial Revolution to the Second World War welfare economics for a post-work society
No full textTracing the evolution of social thought on leisure in Britain from the industrial revolution to the present day, this book documents an alternative and almost totally ignored discourse of leisure as a field of welfare. Investigating evolving understandings of leisure in social philosophy, the nascent social sciences and welfare economics, it explores the ways in which leisure became a field of individual and social welfare in terms of personal growth, cultural democracy and social citizenship. While the social philosophy of ancient Athens remained a reference point, new modern meanings of leisure were forged in the intellectual and political cross-currents of late Victorian and Edwardian political economy, the 'new' liberalism and social ethics. In terms of welfare economics, the book's pivotal figure is John Hobson, a self-declared economic heretic, who adopted Ruskin's idea of intrinsic value as the basis of a new political economy in which leisure would be crucial to individual and social well-being. Providing a unique contribution to the historiography of leisure and welfare and to current debate around wellbeing and work, this is a timely and interdisciplinary book
Crisis Leadership in a Developing Economy: A Study of the Nigerian Insurance Sector Track Leadership and Leadership Development Track
No full textThis study on crisis leadership is motivated by the increasing awareness that companies are dealing with more complicated problems because of unstable economies, poor leadership, and rapid technology development. These factors have intensified concerns about crises leadership and the necessity for proficient approach. Harrison, Paul, and Burnard (2016) underscore the significance of examining leadership and followership across many contexts, particularly in developing countries, since the majority of crisis leadership research is Western-centric and overlooks other locations.This study examines the perceptions of leaders and followers in the Nigerian insurance sector regarding crisis leadership, addressing a gap in the existing literature. Financial volatility, regulatory changes, and weak institutional frameworks are characteristic of the industry and differ from those in Western regions. This study aims to (1) define crisis leadership and (2) analyse its evolution in emerging economies, with an emphasis on follower perceptions
Evaluating an Improvement in Settlement Using Recycled Concrete Aggregate as Stone Column Filler Material
No full textGrowing human population and its conterminous effect on infrastructure development has led to challenges with the availability of soil with good bearing capacity and strong settlement resistance to support these built structures. These challenges have often been addressed by installing pile foundations to secure the structure to a more stable bedrock beneath the weak soil or by replacing the weak soil with one having more potent geotechnical properties. However, these solutions are expensive and time-consuming, especially for low structural loads. Many studies have therefore been conducted to explore techniques for improving in-situ soil properties to avoid the significant cost that will be incurred. Stone columns are mostly used due to their adaptability in improving the bearing capacity and reducing differential settlement in various soils. The sourcing of aggregates for stone columns from quarry sites is an unsustainable approach due to the potential depletion of the natural resource. Innovative and environmentally friendly means of using alternative materials like construction waste have thus been explored. This study focused on using numerical methods to evaluate an improvement in settlement of clayey sandy gravel of South-Central Leeds using recycled concrete aggregate as filler material for stone columns. Analysis of the settlement characteristics of this soil was performed on Settle3 software. From the analysis, total consolidation was reduced by up to 19 % when the sample was reinforced with stone columns made of recycled concrete aggregate. So did an improvement in differential settlement
Fostering inclusive recruitment processes for neurodiverse talent using adaptive artificial intelligence (AI) assessments
No full textInclusion in recruitment remains a critical challenge in the evolving landscape of work, particularly for individuals with neurodivergent cognitive profiles. This study investigates the potential of adaptive Artificial Intelligence (AI) assessments to foster more inclusive hiring practices for neurodiverse talent. Using thematic analysis and simulation-driven data, the research explores how AI systems can be optimized to support equity, reduce cognitive bias, and enhance candidate-job alignment. Grounded in Adaptive Theory, Evidence-Based Rationale, and the Input-Environment-Output (I-E-O) framework, the study models an AI-powered recruitment pipeline using psychometric clustering and retention data. Results reveal that while AI tools can personalise assessments and improve hiring outcomes, they also risk amplifying bias if not calibrated across diverse cognitive profiles. The study emphasizes the importance of neurodiversity-aware model tuning, participatory design involving neurodivergent individuals, and the integration of fairness metrics such as demographic parity and equal opportunity. The findings contribute practical insights and a replicable framework for advancing inclusive hiring through AI-enabled systems
Building AI-Literate Management Graduates: A Multi-Stakeholder Approach
No full textThe increasing integration of Artificial Intelligence (AI) in education and business necessitates the development of critical AI literacy among management graduates. This paper explores how institutional policies, pedagogical strategies, and primary stakeholder engagement can support the responsible use of Generative Artificial Intelligence (GAI) in management education. By analysing existing AI literacy frameworks and institutional practices, the study identifies key gaps and proposes a co-designed AI literacy model tailored for institution ‘A’. Using co-design as a central tenet, the study advocates for AI literacy initiatives that align institutional resources with learner needs and professional domains