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Written evidence for Business and Trade Sub-Committee on Economic Security, Arms and Export Controls - Inquiry on UK economic security
Get PDFExtreme 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
Applying Social Networks to Snowball Sampling of a ‘Hard-to-Reach’ Population and to Illustrate Qualitative Findings
Get PDFEver since Moreno's sociograms were introduced in the 1930s, social network analysis has been a popular way of analysing existing and custom-built data. Social network analysis has been gaining popularity since online social networks were invented with their ever-increasing volumes of social media data available to extract and analyse. This paper explores using social networks as part of the methodology and data analysis stages of an existing research project. The research concerns victims of online crime asking who individuals and organisations can approach for cybersecurity help and advice after becoming online crime victims. Participants worked in UK law enforcement, government, businesses and support organisations. Two networks were built and analysed. The recruitment network monitored snowball sampling of a ‘hard-to-reach’ population-UK adults whose work concerned victims of online crime or who were online crime victims. The organisations' network described the landscape for supporting victims. The recruitment network tracked the recruitment of participants and highlighted successful and influential contacts in the network. The organisations' network explained and illustrated the qualitative findings. Social networks give insights into data missed by other methods of analysing data collected. Sociograms were added to text-based sections in the doctoral thesis to help explain the inherent messiness of the interdisciplinary field of cybercrime
A Novel Lift Adjustment Methodology for Improving Association Rule Interpretation
Get PDFAssociation rules can offer a human-interpretable insight extracted from data. The lift measures used for evaluating association rules in classical Association Rule Mining (ARM) contexts are mainly based on traditional and well-known ones but suffer from interpretation inadequacy when dealing with skewed distributions or low support. This study introduces a new lift adjustment approach with four methods to overcome traditional lift measures and identify the best rules in association rule mining. More concretely, our main objective is to improve the interpretability of association rules to make them more practically relevant for decision-making. We propose an approach incorporating four novel lift adjustment methods (smoothed, weighted, log, and threshold-adjusted lift) to achieve this. We introduce a flexible, dynamic approach combined with four new lift adjustment methods: smoothed, weighted, logarithm, and threshold-adjusted lift. Each technique addresses specific limitations of the traditional lift measure and better captures the reliable representation of item associations by exaggerating stronger relationships or smoothing weaker ones. The proposed methods applied context-aware rule evaluation and adjustment based on measures of relative significance (e.g., Jaccard similarity). The experimental results involving real-world data and synthetic datasets reveal new methods’ effectiveness and robustness in understanding the strengths of association rules and provide a comprehensive view that considers item importance. We evaluate the performance stability of our proposed methods using statistical analysis, including ANOVA, chi-squared, t-tests, and effect size metrics
Response to the Financial Accounting Standards Board’s “Proposed Accounting Standards Update—Government Grants (Topic 832):Accounting for Government Grants by Business Entities”
Get PDFThis paper summarizes a comment letter we submitted to the Financial Accounting Standards Board in March 2025 in response to its Proposed Accounting Standards Update on accounting for government grants by business entities. We submitted a comment letter at the request of the Financial Reporting Policy Committee, which is charged by the Financial Accounting and Reporting Section of the American Accounting Association with responding to requests for comment from standard setters on financial reporting issues. The proposed amendments aim to establish authoritative guidance on accounting for government grants received by business entities. We conclude that the proposed amendments will not provide decision-useful information to financial statement users. We detail the concerns underlying this conclusion and offer recommendations to address them. We also summarize findings from academic research and offer suggestions for future research
Detecting psychometric and diagnostic performance of the RU_SATED v2.0 multidimensional sleep health scale in community-dwelling adults combining exploratory graph analysis and ROC analysis
Get PDFPredicting London’s Precipitation: A Spatio-Temporal Neural Network Approach
Get PDFThis study presents a data-driven approach to forecasting total precipitation in London using an Artificial Neural Network (ANN) within a spatio-temporal framework. Leveraging ERA5 data from 2010 to 2025, the methodology includes automated NetCDF extraction, feature engineering with lagged precipitation and cyclic time encodings, and dimensionality reduction via a trained Autoencoder. The ANN, designed in a GenCast-style architecture, was trained using the Adam optimiser over 50 epochs and achieved strong performance. SHAP analysis highlighted the importance of lag features and seasonal time variables, enhancing interpretability and supporting the model’s application in urban flood risk management and climate resilience
Orthogonal Chirp Division Multiplexing for High-Speed Terahertz Wireless Systems
Get PDF6G systems are expected to operate in the THz bandto support ultra-high data rates. However, severe propagationimpairments in THz channels pose significant challenges. Thispaper presents a new channel model and numerical analysisof a THz system using orthogonal chirp division multiplexing(OCDM). The system includes chirped waveforms at 300 GHz,minimum mean square error (MMSE) equalization, amplitudemodulation, and three ray-tracing-derived indoor environments:spacious hall, long corridor, and empty room. Results showOCDM enhances resilience to multipath effects, especially forlower-order modulations. The findings highlight OCDM withenvironment-aware techniques as a robust solution for futureTHz communication
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