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Intention Reading Architecture for Virtual Agents
Get PDFThis work presents the development of a virtual agent designed specifically for use in the Metaverse, video games, and other virtual environments, capable of performing intention reading on a human-controlled avatar through a cognitive architecture that endows it with contextual awareness. The paper explores the adaptation of a cognitive architecture, originally developed for physical robots, to a fully virtual context, where it is integrated with a Large Language Model to create highly communicative virtual assistants. Although this work primarily focuses on virtual applications, integrating cognitive architectures with LLMs marks a significant step toward creating collaborative artificial agents capable of providing meaningful support by deeply understanding context and user intentions in digital environments
Artificial intelligence for prediction of shelf-life of various food products: Recent advances and ongoing challenges
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Accurate estimation of shelf-life is essential to maintain food safety, reduce wastage, and improve supply chain efficiency. Traditional methods such as microbial and chemical analysis, and sensory evaluation provide reproducible results but require time and labor and may not be suitable for real-time or high-throughput applications. The integration of artificial intelligence (AI) with advanced analysis techniques offers a suitable alternative for rapid, data-driven estimation of shelf-life in dynamic storage environments.
Approach and scope
The current review assesses the application of AI-based techniques such as machine learning (ML), deep learning (DL), and hybrid approaches in food product shelf life prediction. This study highlights how AI can be utilized to examine data from non-destructive testing methods like hyperspectral imaging, spectroscopy, machine vision, and electronic sensors to enhance predictive performance. The review also describes how AI-based techniques contribute to managing food quality, reduce economic losses, and enhance sustainability by ensuring optimized food distribution and reducing waste.
Key findings and conclusions
AI techniques overcome conventional techniques by considering intricate, multi-sourced information capturing microbiological, biochemical, and environmental factors influencing food spoilage. Meat, dairy, fruits and vegetables, and beverage case studies illustrate AI techniques' superiority in real-time monitoring and quality assessment. It also identifies limitations such as data availability, model generalizability, and computational cost, constraining extensive applications. Cloud and Internet of Things (IoT) platform integration into future applications has to be considered to enable real-time decision-making and adaptive modeling. AI can be a paradigm-changing tool in food industries with intelligent, scalable, and low-cost interventions in food safety, waste reduction, and sustainability
The adventure tourist
Get PDFThe aim of this chapter is to provide insights into the consumers of adventure tourism experiences. It firstly begins with an overview of the demand for this type of tourism and some of the challenges involved in accurately estimating this, not least the difficulty in defining what adventure tourism actually is. It then turns to a review of industry demand trends to illustrate the broad range of individuals who engage in adventure activities while on holiday or in their home region. Adventure tourists have diverse needs, motives and profiles. Yet, with the unparalleled growth of the industry over the past 20 plus years, adventure organisations can ensure that this eclectic and abundant group of consumers are well catered for. The chapter then examines the motives which encourage adventure tourism participation and the benefits that adventure tourists enjoy from this. After outlining the inextricable relationship that tourism motives and benefits share, the discussion focuses on adventure recreation motives, which formed the foundation for early research on adventure tourist motives. After a brief review of the more recent scholarly focus on the wellbeing motives and benefits of adventure activities, extrinsic and intrinsic motives and benefits, and push and pull motivation theory are presented. This is followed by a review of the constraints that tourists potentially encounter before and during their adventure experiences, and the negotiation strategies they use to overcome these. Throughout the chapter, there are three illustrative case studies: Sheffield: The Outdoor City (7.1); Older adventure tourists (7.2); and Family adventure tourists (7.3)
Generative AI in Higher Education: Balancing Innovation and Integrity
Get PDFGenerative Artificial Intelligence (GenAI) is rapidly transforming the landscape of higher education, offering novel opportunities for personalised learning and innovative assessment methods. This paper explores the dual-edged nature of GenAI’s integration into educational practices, focusing on both its potential to enhance student engagement and learning outcomes and the significant challenges it poses to academic integrity and equity. Through a comprehensive review of current literature, we examine the implications of GenAI on assessment practices, highlighting the need for robust ethical frameworks to guide its use. Our analysis is framed within pedagogical theories, including social constructivism and competency-based learning, highlighting the importance of balancing human expertise and AI capabilities. We also address broader ethical concerns associated with GenAI, such as the risks of bias, the digital divide, and the environmental impact of AI technologies. This paper argues that while GenAI can provide substantial benefits in terms of automation and efficiency, its integration must be managed with care to avoid undermining the authenticity of student work and exacerbating existing inequalities. Finally, we propose a set of recommendations for educational institutions, including developing GenAI literacy programmes, revising assessment designs to incorporate critical thinking and creativity, and establishing transparent policies that ensure fairness and accountability in GenAI use. By fostering a responsible approach to GenAI, higher education can harness its potential while safeguarding the core values of academic integrity and inclusive education
Financial Turmoil in English Professional Rugby: A holistic performance assessment of English Rugby union clubs (2003-2022)
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Rugby union is encountering financial turmoil on a scale never seen before. League organisers
and governing bodies are calling for reform to protect the future sustainability of the sport. This
paper analyses the financial health of the game at the elite level and provides an intra-industry
comparison of the best and worst performing clubs.
Design/methodology/approach
Data for the research was derived by dissecting the annual accounts of 9 English Premiership
rugby union clubs between 2003 and 2022. Analysis of sporting and financial variables was
performed using a Performance Assessment Model (PAM).
Findings
At club level, there is significant cause for concern in respect of financial health. Many clubs
are simply treading water, maximizing neither financial nor sporting performance. If left
unchecked, there is the acute possibility that some rugby union clubs may cease to exist in the
future which will further damage the commercial attractiveness of the game itself.
Originality/value
There is very little focus on English professional rugby in academic literature which
subsequently means there is a lack of data and empirical evidence that is needed to inform and
drive the structural change necessary for the game to survive and grow in the future. This paper
targets this gap in literature and therefore contributes to the applied sport finance research field.
The paper offers practical recommendations and potential solutions for rugby union to consider
a more financially sustainable future for both member clubs and league organizers, focusing
on the long-term position as opposed to short-term sporting gai
Quality Detection of Common Beans Flour Using Hyperspectral Imaging Technology: Potential of Machine Learning and Deep Learning.
Get PDFCarbohydrate content is one of the most crucial factors in common beans flour (CBF) quality after processing. However, the analysis procedure necessitates the time-consuming and costly selection of elite genotypes from many experimental lines in a destructive manner. Combining hyperspectral imaging (HSI) with machine learning (ML) algorithms provides an effective and fast approach for evaluating the quality of food products. This study determined the quality of CBF by evaluating the contents of carbohydrate using HSI technology. The samples of this work were composed of 12 varieties CBF and each variety was treated by hydration-dehydration method. After various spectral preprocessing steps, spectral features were extracted from the spectral profiles using different feature extraction methods. Partial least square regression (PLSR), Support vector machine regression (SVMR) and Temporal convolutional network-attention (TCNA) were established to predict the contents of carbohydrate in CBF. The best value of R2 and the RMSE and RPD were 0.982, 0.165 and 4.905, respectively by topology of OSC-CARS-TCNA. The outputs demonstrated although deep learning presents more accuracy than ML models, the applied ML models not only provided acceptable and reliable accuracy but also affect significantly in time-analyzing. In addition, visualization output of the current research revealed that the developed models and system can integrate to some intelligent sensors for digitalization aims. This study demonstrates the combination of HSI and ML can be an effective tool in improving the CBF processing industry and providing sustainable and efficient methods in the production of CBF
A Public Conversation about the Past, Present and Future of Home Heating - Clifton Park Museum, Rotherham
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Exploring the Psychoanalytic Dimensions of Sport: An Introduction to Sport and Psychoanalysis
Get PDFThis editorial explores the overlooked, yet compelling, intersection of sport and psychoanalysis. While sport is often viewed as a realm of physicality, competition, and entertainment, psychoanalysis reveals its deeper psychological significance. Sport functions as a site where unconscious desires, fantasies, and social tensions are enacted, challenging the notion that it exists beyond critical thought. This piece introduces several key themes, including the paradox of sport’s (in)significance—its simultaneous frivolity and profound cultural weight—along with the emotional and symbolic investments that shape fan devotion and identity. Sport is presented as a microcosm of broader societal contradictions, where issues like failure, transgression, and unending desire take on heightened meaning. Furthermore, the editorial argues that sport does not merely invite psychoanalytic critique but also poses challenges to psychoanalytic thought itself, particularly regarding embodiment, rule-breaking, and fandom. By recognizing sport as a concentrated form of life, this introduction calls for further scholarly inquiry into its unconscious dynamics. The Sport and Psychoanalysis section welcomes original research and critical reviews on these intersections, emphasizing the necessity of taking sport seriously—not despite its absurdities, but because of them
Optimising Sustainable Deconstruction Strategies for Concrete Structures: A Multi-Criteria Decision Framework Approach
Get PDFPurpose This study proposes a Multi-Criteria Decision Analysis (MCDA) framework to optimise sustainable deconstruction strategies for precast concrete buildings. The framework aims to maximise material recovery, minimises environmental impacts, and ensures cost-effectiveness, providing stakeholders with an evidence-based tool for decision-making. Design/methodology/approach The framework integrates the Analytic Hierarchy Process (AHP) and VIKOR methods to evaluate eighteen deconstruction strategies across four categories: concrete component removal, reinforcement separation, structural dismantling, and material recycling. A case study of a four-storey university building was modelled using DesignBuilder software to simulate material flows and assess performance metrics. Stakeholder input was used to determine criteria weights, balancing environmental, economic, and technical dimensions. Findings (limit 100 words) Selective removal strategies, particularly staircase dismantling (D3) and wall panel removal (D1), yielded optimal material recovery and cost efficiency while minimising environmental and safety risks. Scenario S1 demonstrated adaptability as an early-stage strategy for precast panel removal. Practical implications This framework serves as a practical tool for contractors, policymakers, and environmental agencies, enabling the prioritisation of deconstruction strategies that align with sustainability objectives. It supports global climate goals by reducing emissions and enhancing the value of recycled materials. Originality/value This study advances sustainable deconstruction by integrating AHP and VIKOR into a cohesive MCDA framework. It offers a practical decision-making tool for contractors, policymakers, and environmental agencies, contributing new insights to the construction industry and supporting global sustainability goals