Investigating the effects of energy export options and policies on consumers’ electric vehicle preferences in a low-uptake country

Electric vehicles (EVs) are pivotal for decarbonising the transport sector, yet adoption rates in many countries fall short of what is needed to meet climate targets. Existing research on consumer preferences for EVs predominantly examines high-adoption regions, focusing on established EV attributes and policies. However, as EV technologies evolve and the policy landscape shifts, understanding their impact on shaping consumer preferences in low-adoption markets is critical. This study investigates the influence of advanced energy export capabilities – Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) – and emerging policies on consumers’ EV preferences in a low-adoption market. We use stated preference data collected from a nationally representative sample in Australia. Notably, this is also the first study to quantify the impact of EV-specific road user charges on consumer preferences. The findings reveal that V2G and V2H capabilities significantly enhance consumer appeal, increasing willingness to pay by up to AUD 8991. This is comparable to the willingness to pay increase of AUD 10,006 associated with a purchase subsidy of AUD 5000. Moreover, favourable monetary incentives deliver greater perceived value to consumers. Conversely, non-favourable policies, such as EV-exclusive road user charges, diminish consumer interest, with a 1 cent per km charge reducing willingness to pay by AUD 5415. These findings underscore the transformative potential of EV energy export features to drive adoption, comparable to the effect of financial incentives, while highlighting the necessity of balanced, consumer-focused policy frameworks to accelerate EV adoption in low-adoption markets

Structural Causal World Models for Safety Assurance of AI-based Autonomy

We propose a formal world model, grounded in structural causal models, which we call Structural Causal World Models (SCWMs): interpretable, structured, and machine-verifiable representations of environmental, contextual, and system-internal conditions that define the circumstances under which a system can operate safely. Unlike existing domain-specific approaches, our methodology is domain-agnostic and applicable across diverse safety-critical contexts. By unifying symbolic constraints, probabilistic uncertainty, and causal dependencies, our proposed methodology enables traceable hazard analysis, systematic requirement propagation, and context-aware refinement of safety constraints. We illustrate the methodology through autonomous driving examples, focusing on hazard analysis and safety requirement derivation. More broadly, this work contributes to reducing uncertainty in the safety assurance of AI-based autonomous systems by providing a means of closing the semantic gap in the definition of the system safety requirements associated within complex environments and functions, providing a basis for causal hazard and risk analysis, verification of probabilistic guarantees and run-time monitoring to counteract residual AI model insufficiencies

Memory consolidation during sleep:a facilitator of new learning?

Sleep plays a crucial role in consolidating recently acquired memories and preparing the brain for learning new ones, but the relationship between these two processes is currently unclear. According to the prominent Active Systems Consolidation model, memory representations that are initially reliant on the hippocampus are redistributed to neocortex during sleep for long-term storage. An indirect assumption of this model is that sleep-associated memory processing paves the way for next-day learning by freeing up hippocampal encoding resources. In this review, we evaluate two central tenets of this ‘resource reallocation hypothesis’: (i) sleep-associated memory consolidation reduces hippocampal engagement during retrieval, and (ii) this reduction in hippocampal burden enhances the brain's capacity for new learning. We then describe recent work that has directly tested the relationship between sleep-associated memory processing and next-day learning. In the absence of clear evidence supporting the resource reallocation hypothesis, we consider alternative accounts in which efficient learning is not contingent on prior overnight memory processing, but rather that sleep-associated consolidation and post-sleep learning rely on overlapping or independent mechanisms. We conclude by outlining how future research can rigorously test the resource reallocation hypothesis

Pilot-scale demonstration and practical challenges of bioenergy with CCS (BECCS) using rotating packed bed

This paper presents findings of demonstration of CO2 capture by rotating packed bed absorber using real biomass flue gases. There are two main objectives of the study presented here: (1) performance assessment of pilot scale rotating packed bed CO2 capture absorber with real biomass flue gases (2) the impact of impurities in biomass flue gases on the solvent. The demonstration was carried out at the waste to energy and CO2 capture facilities at the Energy Innovation Centre of the University of Sheffield. Rotating packed bed (RPB) absorber was used to capture CO2 from biomass flue gas generated by a grate boiler. CO2 loadings and solvent concentrations were measured using Mettler Toledo auto-titrator. Particulates content of the flue gas was measured, and particulates were collected for further analysis at the boiler exit and absorber inlet by Electrical Low Pressure Impactor (ELPI®+) manufactured by Dekati®. The particulate samples were analysed by ICP-OES to investigate the impact of metals in the flue gas coming from the biomass on the solvent degradation. Solvent samples were collected and analysed with ICP-MS and Ion Chromatography to quantify build-up of metals and anions in the solvent over time. There is very limited information on this subject in open literature. The short-term tests presented here can serve as a starting point for further longer-term investigations into the impact of biomass flue gas contaminants on the solvent behaviour and the solvent management requirements during CO2 capture from biomass flue gases

Transform(AI)ng Radiology with CheXSBT: Integrating Dual-Attention Swin Transformer with BERT for Seamless Chest X-Ray Report Generation

Radiology reports are crucial for diagnosing diseases, yet generation them is time-consuming, places a significant workload on medical professionals, and is subject to inter-expert variability, as different radiologists may interpret the same X-ray differently. This paper presents a novel hybrid AI model called CheXSBT, which combines our custom-designed Dual-Attention Swin Transformer (DAST) for vision processing with BERT for natural language understanding to automate the generation of chest X-ray (CXR) reports. Leveraging the MIMIC-CXR dataset, which includes over 370,000 X-ray images and their corresponding reports, CheXSBT learns to interpret chest X-ray images and convert them into structured, meaningful text. Our study focuses on two main objectives: (1) automating report generation to accelerate the diagnostic process and (2) improving model interpretability to foster trust among radiologists. The approach involves preprocessing chest X-ray images and their corresponding text reports using the pre-trained BLIP processor, training the novel hybrid vision-language model on paired data, and fine-tuning it for clinical relevance and coherence. The performance of CheXSBT is rigorously evaluated using established metrics such as BLEU, ROUGE, and METEOR, achieving scores of 0.232 for BLEU-4 and 0.392 for ROUGE-L, outperforming other state-of-the-art models and ensuring high-quality report generation. By reducing radiologists’ workload and providing quick, accurate information, CheXSBT aims to transform the intersection between AI and clinical practice, making radiology reporting more efficient, consistent, and accessible

Diffusion with Adversarial Fine-Tuning for Improving Rare Retinal Disease Diagnosis

As machine-aided disease diagnosis becomes more common, there is a rising need for high volumes of quality data, which might be unavailable for rare diseases. Generative methods offer a solution, allowing for synthesising realistic-looking data that can improve diagnosis accuracy. We investigate the applications of diffusion to a small, imbalanced dataset of Optical Coherence Tomography (OCT) images. We propose modifying the basic Denoising Diffusion Probabilistic Model with attention mechanisms, a class-aware training strategy, and the addition of adversarial fine-tuning. We demonstrate that this model is capable of synthesising realistic-looking images with class-specific features even for diseases with as little as 22 samples. We achieve values of FID at 62.58, and CLIP Similarity at 0.96. We show that the addition of generated data in the training dataset improves the overall and class-specific performance of a ResNet18 classifier on the OCT data, offering an improvement for downstream tasks such as rare retinal disease diagnosis

Domain-adaptive diagnosis of Lewy Body disease with transferability aware transformer

Lewy Body Disease (LBD) is a common yet understudied form of dementia that imposes a significant burden on public health. It shares clinical similarities with Alzheimer’s disease (AD), as both progress through stages of normal cognition, mild cognitive impairment, and dementia. A major obstacle in LBD diagnosis is data scarcity, which limits the effectiveness of deep learning. In contrast, AD datasets are more abundant, offering potential for knowledge transfer. However, LBD and AD data are typically collected from different sites using different machines and protocols, resulting in a distinct domain shift. To effectively leverage AD data while mitigating domain shift, we propose a Transferability Aware Transformer (TAT) that adapts knowledge from AD to enhance LBD diagnosis. Our method utilizes structural connectivity (SC) derived from structural MRI as training data. Built on the attention mechanism, TAT adaptively assigns greater weights to disease-transferable features while suppressing domain-specific ones, thereby reducing domain shift and improving diagnostic accuracy with limited LBD data. The experimental results demonstrate the effectiveness of TAT. To the best of our knowledge, this is the first study to explore domain adaptation from AD to LBD under conditions of data scarcity and domain shift, providing a promising framework for domain-adaptive diagnosis of rare diseases

Influence of sleeper geometry on the lateral resistance of bamboo sleepers

This study investigates the effects of bamboo sleeper geometry on lateral track resistance in ballasted railway systems using the Discrete Element Method (DEM), where ballast is modeled as clumped spherical particles with Hertz-Mindlin contact model for non-linear stiffness. Four sleeper geometries, including traditional rectangular, dumbbell, rectangular-winged, and wedge-winged, were analyzed to evaluate their interaction with ballast under lateral loading. The Single Tie Push Test (STPT) procedure was used to measure lateral resistance, with lateral forces applied via a hydraulic jack. The results highlight that sleeper geometry significantly affects lateral resistance, with optimized designs (rectangular- and wedge-winged) achieving the highest resistance values. For the rectangular-winged sleeper, lateral resistance peaked at 6.85 kN, representing a 54% improvement over the traditional rectangular shape. The study also examines the contributions of ballast components (base, crib, and shoulder) to the overall lateral resistance. Base resistance dominated for all geometries but decreases as the crib and shoulder contributions increase with the non-traditional designs. Normal and tangential force distributions within the ballast were also analyzed, showing enhanced interparticle contact for the winged designs. Overall, the rectangular-winged sleeper was found to provide higher performance compared to the traditional prototype sleeper with respect to meeting track requirements for lateral stability

Integrating post-growth economics into transformative adaptation: Property relations, capital, and democratic planning

Transformative adaptation (TA) is increasingly promoted as a way to drive systemic change beyond incremental adjustments. Yet many TA initiatives fall short of their transformative aims. This paper argues that this is not only due to implementation challenges, but also because of conceptual blind spots. We review TA litertaure and problematise three of its core assumptions regarding onto-epistemic and political shifts, root causes of vulnerability, and multi-level stakeholder engagement. Drawing on post-growth economics, we contend that TA tends to overlook some key dimensions of transformation. First, its focus on onto-epistemic and political shifts rarely challenges privatised property relations and sometimes reinforces scarcity narratives that justify exclusive control and management. Second, vulnerability is linked to a preoccupation with growth, efficiency, and commercialisation, yet these are not systematically tied to the structural dynamics of capital accumulation. Third, stakeholder engagement tends to foreground liberal notions of participation and governance, with little attention to democratic control over the economy. To address these gaps, we propose a set of reflective refinements based on post-growth principles: reorient development toward collective, decommodified ownership; link vulnerability to the structural tendencies of capital accumulation; and broaden participation and governance toward economic democratisation and planning. These dimensions are interdependent, with property relations and capital accumulation forming both structural arenas of transformation within, and obstacles to, democratic planning of the economy. We conclude by outlining key directions for future TA research

Uneven development and the geographies of energy transition in Mozambique

In Mozambique, sustainable energy access is an increasing priority for a diverse range of actors seeking to improve livelihoods and stimulate economic development—particularly in rural areas where energy infrastructure remains limited. Drawing on field research conducted as part of a comparative three-year project examining the potential of community energy systems to foster inclusive, just, and clean energy transitions in Southern and East Africa, this paper develops a critical, policy-relevant and geographically grounded analysis of Mozambique’s energy transitions, which are unfolding across multiple fronts. Our analysis addresses both the move away from conventional or ‘traditional’ energy sources to ‘modern’ energy services, and the shift toward renewable energy technologies. We argue that while Mozambique has taken important steps toward a cleaner energy future there remain significant constraints to progress and that it is crucial to consider the advancement of renewable energy in relation to the country’s embedded resource and extractive geographies that shape the directions, possibilities, and spatial dynamics of transition. We examine the broader policy environment, focusing on the state’s energy transition strategy and its implications for energy justice, spatial inequality, and economic opportunity. Particular attention is given to the role and potential of decentralized, off-grid energy systems, emphasizing the need for greater community participation in both policy design and implementation. Finally, we develop a political economy framework to analyse the influence of state institutions, international donors, and private capital in shaping Mozambique’s energy transitions, and assess their impacts on energy poverty and the goal of equitable, sustainable energy access