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

Inter-tropical African precipitation regime shifts dominated by tropical easterly jet

Since the 1990s, inter-tropical Africa (ITA) has experienced consecutive calamitous droughts during the boreal spring. Although the observed precipitation regime changes have been attributed to tropical Indian Ocean-western Pacific warming and/or tropical Pacific La Niña-like cooling, the model-projected past-to-future widespread wetting response to anthropogenic warming overshadows qualitative attributions of decadal shifts in historical precipitation regimes and the reliability of near-term projections. The causes of ITA precipitation regime shifts and the likelihood of their future continuation remain unclear. Here, we reveal that the observed monopolar precipitation changes in ITA are primarily driven by the tropical easterly jet (TEJ)-dominated pattern, with a secondary contribution from the intertropical convergence zone (ITCZ)-mediated pattern. The Indo-Pacific warming-induced TEJ strengthening favors a monopolar drying trend from 1950 to 2022, while the northward-shifted ITCZ drives a west drying-east wetting dipolar pattern. Considering an observational TEJ constraint, an accelerated TEJ with an amplitude of -2 standard deviations could cause an almost threefold increase in extreme drying trends in the near term (2026–2045). Instead, ITA could face a higher likelihood of extreme wetting tendency due to a near-term TEJ weakening. Our findings underscore the importance of realistic TEJ simulations in enhancing confidence in future precipitation projections across hydroclimate-vulnerable Africa

Insights into the role of Ce and Sm in improving low-temperature NH3-SCR performance over Ce-Sm/Cu-SSZ-13 coupled catalysts

The increasingly stringent requirements for controlling nitrogen oxide (NOx) emissions during the cold start conditions of diesel engines serve as a powerful driving force to enhance the low-temperature NH3-SCR performance of state-of-the-art commercial Cu-SSZ-13. In this study, the coupled catalysts were synthesized to create additional active sites for NO oxidation and NH3 adsorption/activation, and the synergistic effect between Cu species and CeO2/Sm2O3 leads to a substantial boost in the low-temperature NH3-SCR activity of CSZ. The results suggest that 6 % Ce-2 % Sm/CSZ, as the optimal coupled catalyst, achieves a NOx conversion of 93.1 % at 200 °C, significantly higher than that of CSZ. The coupled CeO2 and Sm2O3 enhance the number of both Brønsted and Lewis acid sites on CSZ, promoting the adsorption and activation of NH3. Therefore, 6 % Ce-2 % Sm/CSZ can form more NH+4 adsorbed on the Lewis acid sites, which reacts with free ionic nitrates to form NH4NO3. More importantly, the coupled Sm2O3 facilitates the conversion of NH4NO3 by NO to easily decomposable NH4NO2. In addition, additional oxygen vacancies provided by Ce3+ can adsorb O2 and promote the transport of oxygen ions, and electron donation from Sm3+ to [ZCu2+(OH)]+ enhances the low-temperature activity of the latter. Ultimately, the low-temperature NH3-SCR performance of CSZ is improved via a synergistic effect. The NH3-SCR reaction over 6 % Ce-2 % Sm/CSZ co-follows the Eley-Rideal (E-R) and Langmuir-Hinshelwood (L-H) mechanisms

Cosmographic footprints of dynamical dark energy

We introduce a novel cosmographic framework to trace the late-time kinematics of the Universe without assuming any underlying dynamics. The method relies on generalized Padé (2, 1) expansions around arbitrary pivot redshifts, which, compared to state-of-the-art calculations, reduce truncation errors by up to two orders of magnitude at high redshift and yield more precise constraints by defining cosmographic parameters exactly where the data lie. This avoids extrapolations, mitigates degeneracies, and enables a clean disentangling of their effects. Using the latest low-redshift datasets, we center the generalized expansion in multiple bins across z ∈ [0, 1] and obtain precise constraints on the redshift evolution of cosmographic parameters. We find that all key parameters deviate from their ΛCDM predictions in a redshift-dependent way that can be naturally explained within dynamical dark energy scenarios. The deceleration parameter q(z) follows a redshift evolution consistent with the Chevallier–Polarski–Linder (CPL) parameterization, while the generalized Om(z) diagnostic shows deviations of up to ∼4σ from the constant ΛCDM expectation, closely matching the CPL predictions. Taken together, these results point to footprints of dynamical dark energy in the kinematics of the Universe at z ≲ 1

Doxorubicin induces bone loss and modifies multiple cell populations in vivo – Implications for modelling of bone metastasis

Doxorubicin (DOX), commonly used to treat breast cancer, is associated with cardiotoxicity and has negative effects on other organ systems, including the skeleton. DOX-induced bone damage has been demonstrated in murine models; however, results are conflicting due to the use of different doses, schedules, and rat/mouse strains. As DOX is used to limit tumour progression in models of skeletal metastasis, it is paramount to determine how the agent affects the bone microenvironment in the relevant mouse strains, to enable correct interpretation of DOX effects in tumour studies. We have therefore investigated the effects of DOX on bone structure and a range of bone and bone marrow cell populations, comparing immunocompetent and immunocompromised mice. Groups of 7-week-old female BALB/c and BALB/c Nude mice were treated with either saline (control), 4 or 6 mg/kg DOX weekly for four weeks. Effects on bone volume and structure was determined using ex vivo µCT, a panel of bone marrow cell populations were quantified by flow cytometry and osteoblast/osteoclast numbers were assessed using bone histomorphometry. DOX caused trabecular bone loss, with immunocompetent BALB/c mice being more sensitive to DOX than the immunocompromised BALB/c nude counterparts. The 6 mg/kg dose of DOX altered the ratio of bone marrow immune and haematopoietic cell populations in both groups, increasing the numbers of hematopoietic cells and progenitors, decreasing B cells and increasing the number of neutrophils. Bone marrow macrophage and monocyte numbers were increased following DOX treatment in BALB/c nude mice only. Our data demonstrate that DOX impacts a number of cell types in the bone microenvironment, highlighting the importance of considering treatment-induced bone effects when using DOX in models of bone metastasis

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