Learning technology standards development - planning for an improved process and product

This paper presents a framework for improving the legitimacy of learning technology standards by focussing on a better process and product. It is suggested that there is a need for a change in the standardisation paradigm, moving from monolithic to more modular standards

Palmyra Palm Shell (Borassus flabellifer) Properties Part 1: Insights Into Its Physical and Chemical Properties

Bio-based materials are gaining importance in engineering due to their availability, recyclability, and eco-friendliness. Among them, Borassus flabellifer (Palmyra palm) fruit shell (husk) is an underutilized biofiber in Bangladesh, currently limited to disposal or waste-to-energy applications despite its potential for high-value uses. This study explores the physical, chemical, and microstructural properties of untreated Borassus flabellifer husk to evaluate its suitability as a sustainable material for engineering applications. The physical properties, including density, water absorption, moisture regain, and porosity, were assessed according to BS EN ISO 1183-1:2019, ASTM D750, ASTM D2654-22, and ISO 2738 standards. The husk was found to be significantly lighter than its fine as well as coarse fibers and conventional natural fibers like jute, flax, and sisal, making it ideal for lightweight engineering designs. FTIR analysis (qualitatively) revealed the presence of cellulose, hemicellulose, and lignin, which contribute to its mechanical strength, water absorption, and thermal insulation properties, respectively. SEM analysis further demonstrated a cross-linked, porous, and tubular fiber structure, enhancing its thermal and sound insulation features. The findings suggest untreated Borassus flabellifer husk can be a promising alternative for applications requiring lightweight, thermally, and acoustically insulating materials. While its moisture and water resistance outperform some biofibers, chemical treatments could enhance these properties further. To maximize its potential, efficient collection and supply chain systems are essential for industrial-scale production. Harnessing this abundant resource could support sustainable development while encouraging the cultivation and preservation of Borassus flabellifer trees

Dynamic Mechanical Analysis of Borassus Husk Fiber Reinforced Epoxy: Evaluating Suitability for Advanced Aerospace and Automotive Applications

This study investigates the effect of elevated temperatures on the mechanical properties of Borassus husk fiber‐reinforced epoxy composites, focusing on their potential for aerospace internal structural components. Composites were fabricated using Borassus husk fibers incorporated with epoxy resin, including 5% alkali‐treated fibers (treated for varying durations) to improve adhesion. Dynamic Mechanical Analysis (DMA) was performed according to ASTM D5418‐01 standards. Results revealed that both untreated and alkali‐treated fibers enhanced the storage modulus of the composites. The highest loss modulus was observed for the composite with 1‐h treated fibers. The glass transition temperature ( T g ), determined from the peak loss modulus, was significantly higher (84°C–89°C) for treated Borassus husk fiber/epoxy composites compared to neat epoxy and composites reinforced with other natural fibers, such as flax, jute, palm sprout, date palm, sisal, and kenaf. Alkali treatment also notably increased the tan δ (damping factor), with the highest value (1.2) for the 0.75‐h treated fiber composite, outperforming several other natural fiber‐epoxy composites. Cole–Cole plots indicated improved resin‐fiber adhesion for composites containing 0.75‐ and 1‐h treated husk fibers. Phase angle data confirmed enhanced energy dissipation and viscoelastic behavior. Thermo‐mechanical stability improved, with the 0.75‐h treated fiber composite showing the lowest total mass loss (0.4%). Overall, alkali‐treated Borassus husk fiber composites exhibited superior mechanical stiffness, damping capacity, and thermal stability, making them ideal for aerospace and automotive applications requiring strength, impact resistance, and sustainability. It will also contribute to achieving the “net‐zero” target established in the 2015 Paris Agreement

Deep learning approach for stock closing price prediction A hybrid approach using RNN–LSTM architecture

Accurate 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

An investigation into differences in general intelligence and coaches' subjective assessment of players' decision-making skills across different playing positions in EPPP association football academies

With developments in tactical complexity in association football (soccer) general intelligence and decision-making are becoming increasingly important attributes for players at all levels. However, an absence of evidence regarding general intelligence and decision-making across different positions within English Academy soccer indicates that it is unknown how specific intelligence in soccer needs to be for successful performance. This study aimed to 1) examine differences in general intelligence scores between different playing positions, 2) investigate differences in coach assessed decision-making ability between different playing positions and 3) assess differences between general intelligence test score ranks and decision-making ranks awarded by coaches to each player per position. One hundred and one participants, aged 16–18 years were recruited from eight clubs in the English Football League. Participants completed an established psychometric test of general intelligence and the lead development phase coach at each club ranked players' decision-making ability. There were 99 outfield players who participated: 37 defenders, 34 midfielders and 28 attackers. No difference was found in general intelligence scores between playing positions. However, a significant difference was found in decision-making ranks, with coaches determining attacker's decision-making to be lower than midfielders and defenders. Likewise , no difference was found between general intelligence and decision-making ranks for either defenders or midfielders, but a difference was observed between attackers' general intelligence and decision-making ranks. In conclusion, attacker's game intelligence appears to be underestimated by coaches. Consequently, utilisation of a psychometric test of general intelligence could enhance identification of talented players in Academy soccer

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

Experiential 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

Resource orchestration in Indian ethnic entrepreneurial enterprises through generation change in Malaysia

Ethnic entrepreneurial enterprises are continuously evolving, especially when generations change. As these changes take place, resources are also orchestrated differently. However, research gap exists on how resources are orchestrated in ethnic entrepreneurial enterprises through generational change. We answer this question by adopting a qualitative approach based on data from eleven ethnic entrepreneurial enterprises that have experienced generational succession. The data was then analysed by adopting a novel approach of artificial intelligence. Our results suggest that the orchestration in class and ethnic resources has equipped the later generation ethnic entrepreneurs with capabilities to expand and develop their ethnic entrepreneurial enterprises. We emphasize the importance of orchestrating resources in ethnic entrepreneurial enterprises for product innovation, market growth and business development as generations change. The use of artificial intelligence technique enables underlying patterns in ethnic entrepreneurship to be discovered, which assist practitioners in making the best decisions concerning entrepreneurial efforts. This study invites entrepreneurs to comprehend the importance of orchestrating resources for entrepreneurial decision-making in business expansion and development, especially in ethnic entrepreneurial enterprises. With novelty in the methodological application, we extend a cordial invitation to erudite scholars to apply artificial intelligence technique within qualitative research to achieve precision and nuances

Flowers amongst the weeds benefit-finding during the Covid-19 pandemic in England

Preliminary research suggests that in addition to negative experiences, many individuals experienced positive outcomes connected to the COVID-19 pandemic. However, most research has studied posttraumatic growth, which can only account for cognitive positive change, which is a limitation. Therefore, this study aimed to explore experiences of benefit-finding, which includes both practical and cognitive positive changes, relating to living through the COVID-19 pandemic in England within a general population sample.230 participants were recruited via non-randomised convenience sampling. Experiences of benefit-finding were assessed by qualitative self-report via an online questionnaire, distributed as part of a larger mixed methods pandemic study. Results were analysed via inductive content analysis.Approximately 70% of participants reported perceiving at least one benefit because of living through the COVID-19 pandemic. The most commonly reported perceived benefit was having more time to oneself, followed by having more time with family. Other benefits reported included changes to working and education styles, life slowing down and benefits of nature. Overall, the results presented that many individuals felt that the COVID-19 pandemic presented a greater opportunity to make decisions more in line with personal wants/goals. In this way, the COVID-19 pandemic may have presented a unique opportunity for life-crafting.This research provides unique evidence of both benefit-finding and life-crafting in the otherwise negative circumstances of the COVID-19 pandemic in England. Such evidence presents use for understanding factors to support wellbeing in challenging circumstances and for the formulation of potential wellbeing interventions

Detecting emerging patterns in bank card fraud using a neuroadaptive deep learning framework

Bank card fraud is one of the biggest challenges in digital finance space, which needs detection models to address class imbalance, interpretability, and adaptability to changing tactics of fraud. The paper proposes a neuro-adaptive architecture established on a highly structured preprocessing pipeline with stratified splitting, feature normalisation, and representation learning via a Denoising Autoencoder. At the core of this framework lays an Artificial Neural Network optimised by the Firefly Algorithm for fast hyperparameter tuning facilitated by Elastic Weight Consolidation that promotes continual learning without sacrificing past performance. The proposed Adaptive ANN + FA outperforms baseline ANN, CNN, and LSTM models mainly in F1-Score, precision, and recall-the main metrics in fraud detection. Also, SHAP breaks out feature contribution and prediction reasonability making the results very transparent. Optimised adaptive and explainable models are positioned here as strong enablers of real-world fraud discovery and subsequent robustness in the financial systems