19684 research outputs found
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Positioning Social Enterprise as an Engine for Economic Growth:Policy Discourse
No full textThis chapter delves into a detailed exploration of the crucial interplay between social enterprises and economic growth. The primary aim is to conduct a thorough investigation of the multifaceted relationship between these entities. The authors analyse the challenges and advantages inherent in this relationship and propose strategies for policymakers to harness the profound impact that social enterprises can exert on both societal and economic progress. To achieve a comprehensive understanding of this subject, qualitative research methods have been employed throughout this chapter. The research methodology involves an extensive literature review, which serves as a foundation for gathering insights and knowledge pertaining to social enterprises and their intricate ties with economic growth. Recognising social enterprises as catalysts for economic expansion necessitates the formulation of a comprehensive policy framework. Consequently, it becomes imperative to address these challenges. This approach is not only crucial for ensuring the growth and sustainability of social enterprises but also for fostering a form of economic growth that embodies inclusivity, equity, and sustainability. The recommendations presented in this chapter advocate for the establishment of an enabling environment where social enterprises can thrive and make substantial contributions to economic development, all while addressing urgent social and environmental challenges
Prediction of cutting force via machine learning:state of the art, challenges and potentials
Get PDFCutting force is a critical factor that reflects the machining states and affects tool wear, cutting stability, and the quality of the machined surface. Accurate prediction of cutting force has been the subject of extensive research in machining technology for decades. Generally, the predicting methods are based on the physical principles of metal cutting processes and they can be divided into two main categories: calculation of cutting forces by using analytical models and numerical simulation of cutting forces with finite element analysis. With the advance of artificial intelligence and machine learning (ML), various algorithms have been developed to predict cutting force with high accuracy and high efficiency. This paper provides a comprehensive review of force prediction methods, with a focus on ML-based algorithms. The mechanisms and characteristics of various force prediction methods, such as analytical models and finite element analysis, as well as different ML-based algorithms, are introduced in detail. The challenges of current algorithms and their potential in long-term and real-time prediction are discussed. The review highlights the potential of ML-based algorithms in improving the accuracy and efficiency of cutting force prediction and emphasizes the need for further research to address the current challenges and advance the field of force prediction in metal-cutting processes.</p
The life cycle, recyclability and sustainability of plastics used in mass manufacture of single use bottle caps within the UK drinks industry
No full textMass produced single use plastic bottle caps used in the UK drinks and milk industries were examined in this research paper. The main objective and purpose of the research was to assess the sustainability and circularity of the material with other potential food grade materials. Climate change, plastic waste, impact to health, environmental damage, dependency on oil is a great concern for future generations. The three pillars of sustainability metrics: environmental, economic, and social, were used in this research. The methodology used literature reviews, market research, vendor & supplier site visits, exploratory research, and life cycle analysis assessment studies. The research found that material selection & quantity had the largest impacts in the process for overall sustainability and circularity. The analysis showed when HDPE plastic was compared with other materials, recycled Aluminium caps were found the most sustainable overall for the materials assessed. Furthermore, significant sustainability and circularity gains could be made by simply switching from single use to reusable caps found in this research.</p
Casa de Cultura de Girona:GFF 37: We Made Telephones
No full textSpanish premiere of creative archive documentary WE MADE TELEPHONES at the Casa de Cultura de Girona in competition as part of the Girona Film Festival 2025
Federated Q-Learning-Based Optimization for Resource Allocation in Industrial IoT Networks
No full textIn Industrial Internet of Things (IIoT) contexts, efficient predictive maintenance and resource allocation is important to reducing downtime and improving operational performance. This study introduces a novel federated reinforcement learning approach that addresses these difficulties by enabling several agents to learn optimum maintenance policies jointly while maintaining data privacy. Using the Upper Confidence Bound (UCB) technique inside a Q-learning framework, this research dynamically balances exploration and exploitation to obtain the appropriate maintenance activities based on real-time equipment health and resource availability. Experimental findings show the potential for considerable increases in energy efficiency while reducing costs, demonstrating the algorithm’s usefulness in minimizing downtime and maximizing operational efficiency. The creation of a federated training system, as well as the use of UCB for predictive maintenance decision-making, were significant advances. The proposed technique has the potential for implementation in complex industrial contexts, with future work concentrating on integrating advanced predictive models and expanding the algorithm to include multi-objective optimization cases. Furthermore, simulation results illustrate that the proposed method performs 9% better than other methods in terms of energy consumption and 12% in terms of mean episodic reward, demonstrating its efficiency in decision-making and data handling.</p
Trustworthy, Responsible and Ethical Artificial Intelligence in Manufacturing and Supply Chains:Synthesis and Emerging Research Questions
No full textIn recent years, the manufacturing sector has seen an influx of Artificial Intelligence applications, seeking to harness its capabilities to improve productivity. However, manufacturing organisations have limited understanding of risks that are posed by the usage of Artificial Intelligence, especially those related to trust, responsibility and ethics. While significant effort has been put into developing various general frameworks and definitions to capture these risks, manufacturing and supply chain practitioners face difficulties in implementing these and understanding their impact. These issues can have a significant effect on manufacturing companies, not only at an organisation level, but also on their employees, clients and suppliers. This paper aims to increase understanding of trustworthy, responsible and ethical Artificial Intelligence challenges as they apply to manufacturing and supply chains. We first conduct a systematic mapping study on concepts relevant to trust, responsibility and ethics and their interrelationships. We then use a broadened view of a machine learning lifecycle as a basis to understand how risks and challenges related to these concepts emanate from each phase in the lifecycle. We follow a case study driven approach, providing several illustrative examples that focus on how these challenges manifest themselves in actual manufacturing practice. Finally, we propose a series of research questions as a roadmap for future research in trustworthy, responsible and ethical Artificial Intelligence applications in manufacturing, to ensure that the envisioned economic and societal benefits are delivered safely and responsibly.<br/
Ethical Uses of Generative AI in Assessment:Student Perceptions in UK Contexts
No full textUniversity students are increasingly turning to Generative Artificial Intelligence (GenAI) tools for help with their academic assessments, which has prompted major concerns relating to academic integrity. While Universities globally are building guidance on good practice in the use of GenAI, there is a lack of empirical understanding of student perceptions of what ethical and equitable use means to them. Developing insight into student understanding of GenAI is important in enabling institutions to offer more appropriate training and support to encourage good practice in assessment, not just from the perspective of avoiding malpractice, but in identifying ethical opportunities for integrating the technology as a useful tool. Using the GenAI literacy framework as the theoretical foundation, this paper analysed students’ GenAI usage in academic assessments within the UK context. Data were collected from 80 participants through focus groups conducted by four UK institutions. Our findings show that students are exploring the potential of GenAI as a tool and are beginning to understand where the ethical boundaries might sit. They are keen to use the technology to support their learning but have significant concerns about locating that boundary between good practice and that which exposes them to accusations of cheating, or which limits their own learning. The paper reinforces the importance of providing GenAI literacy training to university students, so they may develop a better understanding of how GenAI can support learning processes in an ethical way
A Data-Driven Approach for Fibres Recognition via Spectrophotometry
No full textThe increasing volume of textile waste presents significant environmental and economic challenges, necessitating the development of efficient automated sorting techniques to support a more effective textile waste recycling. Automated sorting is a notoriously complex task, due to deployment constraints and to the variability of textiles. To advance the work on automated textile sorting, this study investigates the use of data-driven approaches on spectrophotometer-based reflectance measurements for recognising fibres. Spectrophotometry offers significant advantages in terms of operational simplicity and reliability, making it a promising choice for use in textile sorting facilities where environmental conditions are difficult to control. Considering an extensive dataset of specifically acquired pure textile samples, in this work we leverage on AutoML solutions to determine the best architecture to discriminate between cotton and polyethylene terephthalate (PET) fibres
The UK Northern Talking Therapies Practice Research Network:Lessons from ten years of generating practice-based evidence
No full textPractice research networks (PRNs) have been proposed as a mechanism to support continuous service evaluation and improvement in the field of psychological therapies. In theory, PRNs could help to generate high quality practice-based evidence that has potential to inform and improve clinical care. However, in practice, many obstacles pose challenges to the sustainability and impact of such networks. The UK Northern Talking Therapies PRN is an exemplar that has generated over 20 scientific publications over a decade of successful clinical-academic collaborations. This article distils key lessons learned over that time, to guide and promote the wider adoption of PRNs in psychological services
Climate change, global population and the capitalist axiomatic:making sense of Malthus
No full textThis paper critiques the neo-Malthusian proposition that drastic global population reduction is essential to counteract the current climate emergency. It applies a new materialist, rhizomatic and more-than-human ontology of capitalism based Deleuze and Guattari’s analysis of the ‘capitalist axiomatic’: the free flows of commodities, capital and labour characteristic of capitalist production. Three sections address these rhizomatic flows, considering ‘extractive capitalism’, economic growth, and population growth through this DeleuzoGuattarian lens. The paper concludes, contra Malthus, that while growth in the global human population is placing strain on the environmental capacity to support it, it is the complex, rhizomatic interactions within the capitalist axiomatic that are producing anthropogenic climate change. Remedies short of a wholesale global shift from a capitalist economy are proposed to address this global crisis
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