Julian Padget

The rapid growth of electric vehicle (EV) adoption presents significant challenges for electricity networks, particularly at the low-voltage level, where clustered neighbourhood demand risks overloading infrastructure. Existing scenario-based planning approaches typically assume uniform EV uptake across neighbourhoods within a region, failing to capture the heterogeneity in historical EV registration data. They provide limited uncertainty quantification, despite the difficulty of predicting future adoption at fine spatial scales. This paper introduces a Gaussian process (GP)-based forecasting framework that combines granular historical EV registration data with top-down regional scenarios to generate probabilistic neighbourhood-level forecasts. The GP captures how local adoption deviates from regional trends, encoded in the GP’s mean function, ensuring consistency with broader scenarios while accounting for local variation and uncertainty. We validate the framework using ten representative local authority districts in England and Wales, covering 1,294 neighbourhoods. The framework demonstrates improved performance compared to baseline methods (scaled scenario, logistic growth, linear extrapolation) in normalised mean absolute error, with statistically significant improvements at horizons of three years and beyond. It also delivers well-calibrated prediction intervals, providing reliable uncertainty estimates. This framework offers a practical tool for network operators, policymakers, and planners to support targeted decision-making and investment.

Verification, Validation and Testing (VVT) of complex cyber-physical systems, such as autonomous vehicles, have traditionally relied on document-intensive processes and physical prototypes. However, accelerated development timelines increasingly challenge the feasibility of these approaches. Model-Based Systems Engineering (MBSE) offers a model-centric alternative that enables new forms of system analysis. This paper presents a graph-based methodology for evaluating System Modelling Language (SysML) system models using functional flow block diagrams and interface definitions. By transforming system functions into a Functional Dependency Graph and applying community detection techniques, we derive three structural metrics: System Complexity, System Modularity, and System Test Effort. The methodology is applied to three case studies, an academic autonomous mobile robot and two industrial automotive propulsion systems, to explore its potential utility. Across these examples, the graph-based approach yielded improvements in all three metrics compared to traditional SME-based partitioning. The findings suggest that graph-based evaluation may support early-stage architectural reasoning and model refactoring in MBSE workflows, with further validation needed to confirm its broader applicability and practical impact.

This paper provides a detailed overview of the current snapshot of available open data for modelling the impacts of electric vehicles (EVs) on the UK distribution network, highlighting opportunities for a digital spine. We are the first to review open data available for UK distribution networks, focusing on spatial data. We also explore data for census small geographies, vehicle ownership, EV charger locations and data on their usage. Several issues are identified, including inconsistencies in dataset availability, file naming conventions, feature definitions and geographic discrepancies. We specifically analyse EV charger connection data for secondary distribution substations from two UK Distribution Network Operators (DNOs). The validity of the data is assessed by comparing it to known public charger locations from OpenChargeMap. While one DNO provides data coverage for >95% of its substations, it is valid for only 24.1% of substations with at least one public charger. Conversely, the other DNO provides data coverage for 1% of its substations due to privacy‐related obfuscation, with data valid for 98.3% of substations with at least one public charger. Addressing these challenges through standardised data‐sharing practices and implementing a digital spine could enhance the accuracy and reliability of EV‐grid integration models. These improvements are essential for facilitating the seamless integration of EVs into the grid and supporting the transition to a sustainable energy system.

CLIP is a widely used foundational vision-language model that is used for zero-shot image recognition and other image-text alignment tasks. We demonstrate that CLIP is vulnerable to change in image quality under compression. This surprising result is further analysed using an attribution method-Integrated Gradients. Using this attribution method, we are able to better understand both quantitatively and qualitatively exactly the nature in which the compression affects the zero-shot recognition accuracy of this model. We evaluate this extensively on CIFAR-10 and STL-10. Our work provides the basis to understand this vulnerability of CLIP and can help us develop more effective methods to improve the robustness of CLIP and other vision-language models.

The study of autonomous agents has a long history in the Multiagent System and the Semantic Web communities, with applications ranging from automating business processes to personal assistants. More recently, the Web of Things (WoT), which is an extension of the Internet of Things (IoT) with metadata expressed in Web standards, and its community provide further motivation for pushing the autonomous agents research agenda forward. Although representing and reasoning about norms, policies, and preferences is crucial to ensuring that autonomous agents act in a manner that satisfies stakeholder requirements, normative concepts, policies, and preferences have yet to be considered as first-class abstractions in Web-based multiagent systems. Towards this end, this article motivates the need for alignment and joint research across the Multiagent Systems, Semantic Web, and WoT communities, introduces a conceptual framework for governance of autonomous agents on the Web, and identifies several research challenges and opportunities.

The study of autonomous agents has a long tradition in the Multiagent Systems and the Semantic Web communities, with applications ranging from automating business processes to personal assistants. More recently, the Web of Things (WoT), which is an extension of the Internet of Things (IoT) with metadata expressed in Web standards, and its community provide further motivation for pushing the autonomous agents research agenda forward. Although representing and reasoning about norms, policies and preferences is crucial to ensuring that autonomous agents act in a manner that satisfies stakeholder requirements, normative concepts, policies and preferences have yet to be considered as first-class abstractions in Web-based multiagent systems. Towards this end, this paper motivates the need for alignment and joint research across the Multiagent Systems, Semantic Web, and WoT communities, introduces a conceptual framework for governance of autonomous agents on the Web, and identifies several research challenges and opportunities.