17837 research outputs found
Sort by
Bus‐Based Sensor Deployment for Intelligent Sensing Coverage and k‐Hop Calibration
Get PDFDrive‐by sensing is a promising concept that employs public transport as a mobile sensing platform to achieve high spatio‐temporal coverage for urban sensing tasks. At the same time, the low‐cost nature of mobile IoT sensors necessitates their more frequent calibration to ensure data accuracy and reliability. Manual or lab‐based calibration of a large number of mobile sensors may no longer be feasible and thus new approaches for automatic calibration are needed. Most prior work on optimal mobile sensor deployment focuses on coverage aspect without considering the sensor calibration. In this study, we present a joint approach for optimising the placement of bus‐based sensors for maximising the total unique sensing area and combining the optimal reference sensors geo‐placement for maximising k‐hop calibrate requirements on the selected routes. A metric‐based system developed in our model uses geographical set operations which includes both spatial and temporal joins to quantify the contribution of each bus route and rank them accordingly. We formulate the coverage optimisation problem as a mixed integer linear program (MILP), solve it with a greedy algorithm, and demonstrate this method’s potential using real‐world bus‐transit data from Toronto, Canada and Manchester, UK. Our approach involves a metric‐based system which quantifies each bus route unique coverage contribution for determining an optimal set of bus routes and bus stops for bus‐based and reference sensor deployment, to minimise sensor network costs and maximise spatio‐temporal coverage. The comparison with a random baseline algorithm indicates that our method outperforms in terms of deployment and coverage efficiency. Our results also include the potential of our weighted method in improving drive‐by sensing for air quality monitoring by comparing it with a separate benchmark scheme with different criteria
Revolutionising Marketing Education – A Sociocultural Approach to Praxis Pedagogy
Get PDFThis paper examines how the changing marketing landscape necessitates transformative learning approaches capable of preparing students for responsible and collaborative practice of marketing. It considers the potential of a Freirean praxis approach to marketing education informed by sociocultural theory to propose a framework for transformative learning.This is a conceptual piece integrating insights from Freire’s pedagogy and Vygotsky’s theory to propose a praxis pedagogies-based sociocultural framework for marketing education and ultimately employability.The paper identifies a set of key technological, environmental and societal factors impacting marketing and marketing education. In response to these, an integrative framework is provided to prepare students for the current and future challenges arising. Given the nature of the factors identified, this framework proposes a pedagogical approach that is not only critical but also socio-culturally informed.It adds to the burgeoning literature on responsible marketing education offering a theoretical and practical framework.This framework provides marketing educators with practical tools. Grounded in praxis and sociocultural theory, it equips students with critical thinking and collaboration skills, preparing them as marketers in response to societal and technological challenges.This approach is built on theory and exemplars directed at positive social change.The novelty of this pedagogical approach derives from the unique integration of theoretical perspectives from praxis and sociocultural theories towards transforming marketing education and thus practice in directions that are sustainable, socio-culturally grounded, and participatory
A Quantum of Learning: Using Quaternion Algebra to Model Learning on Quantum Devices
Get PDFThis article considers the problem of designing adaption and optimisation techniques for training quantum learning machines. To this end, the division algebra of quaternions is used to derive an effective model for representing computation and measurement operations on qubits. In turn, the derived model, serves as the foundation for formulating an adaptive learning problem on principal quantum learning units, thereby establishing quantum information processing units akin to that of neurons in classical approaches. Then, leveraging the modern HR-calculus, a comprehensive training framework for learning on quantum machines is developed. The quaternion-valued model accommodates mathematical tractability and establishment of performance criteria, such as convergence conditions
Extreme Weather Impacts on Microgrid Components:A Critical Review Establishing Data-Driven Methods as the Definitive Path Forward
Get PDFTo address climate change, there is an acceleration of the integration of renewable energy (RE) technologies into power systems (including microgrids(MGs)) worldwide, with forecasts indicating that RE sources (RES) will be responsible for meeting approximately 50% of global energy consumption by 2025. While this transition supports meeting national and global targets and sustainability goals, it introduces significant operational challenges to electricity networks due to the weatherdependent nature of RE generation whilst there is growing electricity demand for heat, transport and other sectors. MGs have emerged as a popular option for meeting the growing demands from electric vehicle (EV) fleets and other emerging loads while enhancing the reliability and resilience of distribution networks (DNs). However, the renewable resources and components within the MGs also remain vulnerable to extreme weather conditions. This paper presents a comprehensive review of extreme weather impacts on key renewable-based MG components and hence the MG’s overall operations. Specifically, we discuss how extreme meteorological conditions can affect performance parameters, reliability metrics, and control requirements of key components like photovoltaic (PV) systems, EVs, and battery energy storage systems (BESS) within an MG. We conclude that robust weather aware data-driven frameworks and tools utilizing advanced forecasting algorithms are necessary to improve MG stability, efficiency, and operational security
Exploring the role of management accounting in building sustainability and resilience
Get PDFThe COVID-19 pandemic highlighted and exacerbated pre-existing social, economic, and governance challenges while creating new complexities. More recent economic and geo-political developments such as the Russia-Ukraine War, global inflation surge and major adverse weather events have presented new challenges. As management accountants navigate these issues, they are positioned as key players in addressing immediate crises and fostering long-term adaptability. They can redefine their role to play an increasingly significant part in organisational resilience, tasked with ensuring that organisations are equipped to withstand and adapt to various disruptions while maintaining long-term, sustainable practices.Resilience has been understood in different ways, including in relation to stability, recovery, adaptation, and growth. However, the lack of a unified framework has led to a “jingle-jangle fallacy” where different approaches to resilience coexist without clarity. While the literature on resilience reveals a range of framings and definitions across disciplines, it is commonly defined as the ability of individuals, teams, or organisations to adapt, recover, and thrive in the face of adversity. In this report, we apply this understanding as the lens for exploring the role of management accounting in building sustainability and resilience.Management accounting scholars have long investigated the role of professionals and organisations in supporting decision-making in the context of sustainability strategy formulation and implementation, measuring and assessing sustainability, sustainability reporting and disclosure, supply chain sustainability, circular economy and resource efficiency.Sustainability is defined here as a triple-bottom-line concept encompassing environmental, social, and financial viability. While links between sustainability and resilience are often acknowledged, empirical evidence remains scarce on how management accounting can effectively support both. This report aims to provide insights and practical guidance to support management accountants and organisations in developing integrated approaches that effectively align sustainability initiatives with resilience strategies. The findings show two key themes:1. The first theme highlights how organisations are evolving their management accounting practices to enhance resilience in response to external disruptions, focusing on dynamic budgeting, enhanced risk management, supply chain transparency, stakeholder engagement, and innovation.2. The second theme sheds light on how organisations have integrated sustainability metrics into management accounting, providing a balanced view of financial performance alongside environmental and social responsibilities. Although our study focuses on garment firms, the practices we document (such as managing complex supply chains, accelerating forecasting cycles, embedding ESG metrics, fostering cross-functional collaboration, and leveraging data-driven transparency) address challenges that are common to many industries facing volatility, regulatory change, and heightened stakeholder expectations. These findings reflect broader opportunities for management accountants in firms that operate across diverse sectors, particularly those with extensive supply chains, a mix of mass-market and premium positioning, and a growing need to integrate data analytics, ESG indicators, and scenario planning into their financial control processes. Accordingly, the insights and recommendations presented in this report can be adapted beyond the garment industry to support more resilient and sustainability-oriented management accounting practices across sectors. <br/
‘Doing adoptive family’ in contemporary India – A qualitative exploration of adoptive parents’ narratives
Get PDFThis article discusses adoptive family practices in India, where biological connections are often seen as more important than social ties and legal recognition in building family relationships. It shares insights from a unique, in-depth study of 11 adoptive parents, connecting their experiences to the concept of ‘family practices’. The article highlights two main points. First, it shows thatadoptive families vary in how they create their family lives, especially in response to everyday challenges. These families seek to socially legitimise their understanding of kinship. Second, a continuous process of negotiation and renegotiation is required to create and redefine relationships to demonstrate their familial relationships. While the small group of families studied does notrepresent all adoptive families in India, the findings reveal the complexities of adoptive family life in a contemporary context. This study provides a strong springboard for further research and can improve social work policy and practice
Finding the paper behind the data:Automatic identification of research articles related to data publications
Get PDFData papers are scholarly publications that describe datasets in detail, including their structure, collection methods, and potential for reuse, typically without presenting new analyses. As data sharing becomes increasingly central to research workflows, linking data papers to relevant research papers is essential for improving transparency, reproducibility, and scholarly credit. However, these links are rarely made explicit in metadata and are often difficult to identify manually at scale. In this study, we present a comprehensive approach to automating the linking process using natural language processing (NLP) techniques. We evaluate both set-based and vector-based methods, including Jaccard similarity, TF-IDF, SBERT, and reranking with large language models. Our experiments on a curated benchmark dataset reveal that no single method consistently outperforms others across all metrics, in line with the multifaceted nature of the task. Set-based methods using frequent words (N=50) achieve the highest top-10% accuracy, closely followed by TF-IDF, which also leads in MRR and top-1% and top-5% accuracy. SBERT-based reranking with LLMs yields the best results in top-N accuracy. This dispersion suggests that different approaches capture complementary aspects of similarity (lexical, semantic, and contextual), showing the value of hybrid strategies for robust matching between data papers and research articles. For several methods, we find no statistically significant difference between using abstracts and full texts, suggesting that abstracts may be sufficient for effective matching. Our findings demonstrate the feasibility of scalable, automated linking between data papers and research articles, enabling more accurate bibliometric analyses, improved tracking of data reuse, and fairer credit assignment for data sharing. This contributes to a more transparent, interconnected, and accessible research ecosystem
Data sources:
Access Repository Dashboard
Do you manage Royal Holloway - Pure? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!