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Unit 14: using reported data: a teaching approach.
Get PDFThis presentation introduces Unit 14 from Accounting Streams as a teaching module for accounting education that gives students experience of analysing real-world financial data from specific companies
Assessing whole life carbon impacts of retrofitting Canada's 19th and early 20th century homes towards net-zero.
Get PDFThe ongoing climate crisis provides a unique challenge within the residential sector to reduce greenhouse gas emissions by 2050 in response to Canada's net-zero goal. Although legislation has been put in place to address the decarbonisation of the existing housing stock, the challenging goal set by the Canadian government requires action beyond current energy guidelines. This poses a difficult challenge regarding the retrofit of housing built in the 19th and early 20th century. The extensive retrofit needed to bring these houses to the highest level of residential sustainability may counteract the idea of national zero-carbon when considering embodied carbon. This paper seeks to evaluate the whole-life carbon impacts of implementing deep retrofits in 19th and early 20th-century Canadian homes using the EnerPHit standard. To achieve this objective, the study compares whole-life carbon emissions under a non-retrofit baseline and a retrofit scenario that adheres to the EnerPHit standard, focusing on homes in Southern Ontario. HOT2000 Energy simulation software was used to assess the operational energy consumption, while One Click LCA was used to evaluate the whole life carbon of the two scenarios. This study found that the deep retrofit resulted in a 60% reduction in annual operational emissions compared to the baseline scenario. The whole-life carbon assessment further demonstrated a 48% reduction in total emissions over a 60-year period, with a carbon payback period of 6.8 years. These results imply that adapting this retrofit strategy would be beneficial to meeting Canada's 2050 net-zero goals, as the operational energy savings are superior to the total embodied carbon associated with the retrofit strategy
Revisiting radical integrated research and practice for contemporary social work.
Get PDFThis article explores and revisits a radical research framework for contemporary social work. Our article reflects on how an integrated and enriched social analysis supports social work understanding, intervention, and an evaluation of professional practice. This framework, which includes Marxist approaches, recognizes that social work is a political profession (Reisch and Jani 2012; Cummins 2019). The profession is involved in addressing the challenges experienced by people to manage the contradictions of capitalism and are thereby also involved in sustaining the capitalist system (Tavares 2013). Consequently, the profession is caught in a conflictual position whereby it needs to meet the demands of the State, while also being committed to addressing the needs of the working class. We propose that a radical analysis of the contexts and lived reality of individuals, groups, communities, institutions, and societies within capitalist societies is necessary to understand the contemporary practice milieu, and to ensure consistent and effective professional interventions. We conclude by noting that this research framework aligns with the International Federation of Social Work social work definition and enables the profession to address social inequality, oppression, and social justice. This article explores an integrated practice research framework for contemporary social work, encouraging at its core the importance of social analysis to broaden understanding, intervention, and evaluation as the tool of professional social work. The profession role often involves addressing the resulting problems experienced by people due to the contradictions of capitalist society. A radical analysis of the contexts within capitalism is required to appreciate the contemporary practice context, to ensure consistent and effective professional interventions at multiple levels. This radical research framework supports the professions' role in addressing social inequality, oppression, and social justice
Social work: the importance of social, economic and political analysis to develop frameworks for intervention.
No full textThis chapter will explore two essential inter-connected social work professional dimensions, namely the socio-economic and political, which should form a key part of the analysis undertaken by the profession before they seek to intervene, either individually or collectively. It explores the implications for social workers who may often be required to manage scarce resources within complex and bureaucratic managerial systems, while often having little or no substantial political or professional involvement in the preparation of these budgets their implementation, monitoring, and control. It explores a key challenge for the profession in its day-to-day work, where it is required to address the impact of poverty, while implementing political strategies on those most disadvantaged and constrained by "inadequate" resources at national and international levels
Safety on the horizon: mapping human factors in offshore wind.
Get PDFGlobal wind energy is experiencing an unprecedented period of expansion. Such requirements present challenges concerning maintaining safety standards and training at scale with industry data indicating a range of hazards during operations and maintenance activities. Wind technicians work in small teams to conduct critical operations and maintenance tasks on wind turbines often in remote, hazardous environments. Industry evidence suggests behavioral issues significantly contribute to these incidents, yet the limited human factors (HF) research in offshore wind typically focuses on design and physiology. Three online stakeholder workshops identified the principal HF impacting offshore wind technicians during operations and maintenance activities. It resulted in a preliminary framework of 16 HF, and 16 sub-factors, for offshore wind technicians encompassing individual, crew/team, organizational factors, and task and environmental factors. This can be used to direct effective interventions that support worker safety, health and performance in the expanding wind industry and wider renewable energy sector
Family businesses in the Arab world.
No full textThis chapter discusses the dynamics of family businesses in the Arab world, emphasising their unique interplay between familial, cultural, and institutional factors. It highlights the significant role of family businesses in the region's economies, contributing substantially to GDP and employment. The chapter explores key themes, including the influence of collectivist values, patriarchal norms, and Islamic principles on governance, innovation, and succession planning. Using a context-sensitive lens, it examines the challenges and opportunities presented by institutional environments across different Arab countries. Case studies illustrate how these businesses navigate generational transitions, gender roles, and diversification strategies while maintaining family legacy and identity. By applying a family business dynamics framework, this chapter provides insights into the distinct features and contextual variations shaping the success and sustainability of Arab family enterprises
Integrating predictive and hybrid machine learning approaches for optimizing solar still performance: a comprehensive review.
Get PDFThe increasing global need for freshwater, coupled with the imperative for sustainable and energy-efficient solutions, has fueled interest in solar distillation technologies. Solar stills (SSs) offer a simple, low-cost and environmentally friendly approach to desalination. However, their performance can be significantly influenced by various factors, including climatic conditions, design parameters and operational variables. To address these challenges and predict SS performance, machine learning (ML) techniques have emerged as a powerful tool. This review explores the application of various ML models, including Support Vector Machines (SVM), Multi-Layer Perceptrons (MLP), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Decision Trees (DT) and hybrid ML/metaheuristic optimizer models - such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) - in predicting water production rates, managing energy consumption and providing decision support for operators. The review highlights the potential of these models to enhance the efficiency and sustainability of solar desalination systems. By leveraging data-driven insights and predictive modeling, ML-based approaches enable the prediction of performance metrics, identification of optimal operating conditions, and real-time monitoring and control. Furthermore, hybrid ML/metaheuristic models, which combine algorithms like SVM, MLP and ANFIS with optimization techniques, offer enhanced reliability and resilience in complex scenarios. This review emphasizes the significant potential of ML in advancing solar distillation technologies, showing that integrating ML techniques into SS systems can lead to more efficient, sustainable and cost-effective solutions to address global water scarcity challenges
Few-shot essay grading: weighted prototypical networks for ordinal text classification.
Get PDFAutomated Essay Scoring (AES) presents a key opportunity to improve student experience while reducing the administrative burden of academic staff. However existing methods for AES are reliant on large volumes of data and fail to consider the ordinal aspect of grading. As a result, when an institution introduces a new assessment, there may be no data available to train algorithms. In this paper, we demonstrate that metric learning architectures, specifically Prototypical Networks, offer robust performance on few-shot ordinal classification essay grading tasks. We introduce three novel weighted prototype calculation strategies designed to enhance class representation in ordinal few-shot text classification. These strategies improve how class knowledge is modeled from limited examples by refining the way prototypes are computed, incorporating weighted mechanisms for better differentiation. Results across four datasets show that our methods outperform existing baselines and the current state-of-the-art in ordinal few-shot text classification. Additionally, we compare our approach with three large language models (LLMs) using a prompt-based approach to few-shot learning and find that we achieve superior or comparable performance in all evaluated tasks
Dimensionality reduction for enhancing malware classification accuracy in portable executable files.
Get PDFPortable executable (PE) files are a common vector used for the spread of malware. This paper reviews and evaluates machine learning-based PE malware detection techniques. A dataset was constructed using malicious samples from Virus Share and benign samples from github. Static analysis was used to extract highly ranked features, followed by dimensionality reduction using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). K-Nearest Neighbors and Random Forest classifiers performed well, achieving accuracy between ≈93% and ≈94% when combined with LDA. By integrating static analysis with dimensionality reduction, this study provides new insights into optimising machine learning performance for malware classification
The UK Supreme Court on biological sex: a tale of two legal perspectives. [Blog post]
Get PDFIn this blog post, the authors reflect on the 2025 decision by the UK Supreme Court regarding the interpretation of the Equality Act 2010, where the latter uses words to protect women and members of the trans community against discrimination. The authors provide some legal context for the decision and reflect on two perspectives that give rise to fundamentally different understandings of legal rights under the Equality Act 2010
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