Influence of particle morphology and solvent choice on the sublimation of recrystallised ibuprofen at ambient pressure and sub-melting temperatures

The tendency of ibuprofen to sublimate can undermine content uniformity and cause caking during storage, resulting in significant economic losses. While previous studies have predominantly investigated typical evaporation under vacuum, the solvent-mediated relationship between crystal structure, sublimation kinetics and thermodynamics under practical conditions remains unclear. This study aimed to determine how crystallisation solvent governs ibuprofen sublimation under ambient pressure and sub-melting temperatures, independent of particle size, by linking crystal structure and morphology to sublimation behaviour. Ibuprofen was recrystallised from hexane, acetonitrile, ethanol and methanol, with composition and structure verified using FTIR and PXRD. SEM and particle size analysis quantified morphology and surface area, while sublimation kinetics were measured by TGA and DVS under storage-relevant conditions and subsequently enthalpy of sublimation and change in vapour pressure by temperature were estimated. It was found that polar-solvent crystals have higher surface energy and faster sublimation, while non-polar-solvent crystals were more stable and slower sublimation. The obtained enthalpy of sublimation was lower for polar-solvent samples and estimated change in vapour pressure by temperature aligned with literature trends. Overall, crystallisation solvent is a critical determinant of ibuprofen sublimation under storage-relevant conditions, influencing plane orientation, crystallinity and morphology. Polar solvents promote faster dissolution and potentially enhanced therapeutic performance but increase sublimation risk, whereas non-polar solvents improve storage stability at the expense of dissolution rate. These findings provide practical guidance for optimising solvent selection to balance pharmaceutical performance, stability and manufacturing efficiency

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

Relationships between heritable dementia risk factors, cardiovascular risk factors in young adulthood, and midlife neuropsychological outcomes

Background Selected cardiovascular factors, APOE4 carriership, and family history (FH) are robust risk factors for Alzheimer’s disease and dementia. While cardiovascular risk tends to affect cognition from midlife, it remains unclear whether heritable risk predicts cardiovascular health in young adulthood and midlife, and whether young-adult cardiovascular health predicts midlife cognition. Objective We sought to examine how heritable dementia risk relates to cardiovascular health and how these cardiovascular risk factors in young adulthood predict midlife brain volumes and cognition. Methods We used data from the CARDIA study, which followed 5115 individuals aged 18-30 at baseline over 30 years. Analyses focused on 2808 participants (Mean age = 60, SD = 3.58) who attended the 30-year visit. We examined associations between APOE4 and FH with baseline and 30-year follow-up measures of cardiovascular risk factors (LDL-C, HDL-C, glucose, blood pressure, body mass index (BMI), smoking), cognition, and brain volumes. Results APOE4 carriers with FH had higher LDL-C and lower HDL-C levels as early as young adulthood, persisting into midlife. BMI and smoking were the only cardiovascular risk factors from young adulthood that predicted midlife cognition. There was no association between young adult cardiovascular risk factors and midlife brain volumes, but those with heritable dementia risk had larger brain volumes in regions vulnerable to midlife atrophy. Conclusions APOE4 carriership was associated with an unfavourable lipid profile that started in early adulthood and persisted to later life. Early cardiovascular risk was also associated with midlife cognition, which is earlier than studies typically focusing on later-life cognition

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

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

Mitigating future glacial lake outburst floods in the Himalaya

Glacial lake outburst floods (GLOFs) are among the most severe cryospheric hazards in the Himalaya. While previous studies have primarily focused on the characteristics and causes of GLOFs, strategies for mitigating their disaster impacts remain underexplored. This study introduces China’s Glacial Lake Management System (GLMS) and evaluates its potential for regional replication in reducing damage caused by GLOFs. We find that while GLOF frequency shows a statistically insignificant decrease from 1990 to 2023, downstream damage has intensified, yet appears relatively mitigated within China across the Himalaya following the implementation of the GLMS. Further hydrodynamic modelling suggests that glacial lakes will continue to expand in the future, with total growth expected to triple relative to the 2000–2020 period. These expansions could increase GLOF exposure by over 27% for high-risk lakes and by more than 40% in regions outside China without targeted interventions. However, implementing GLMS engineering measures could reduce the intensity of future floods by 24%, with even greater reductions outside China—29% compared to 21% within China. Building on China’s lake management experience and recognizing the transboundary nature of GLOFs, the comprehensive framework we propose for region-wide glacial lake risk reduction across the Himalaya integrates engineering measures, early warning systems, and community responses. This framework addresses the urgent need for proactive and coordinated mitigation strategies in densely populated high-mountain regions

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

Plant traits as potential drivers of timber value in the Dipterocarpaceae

Southeast Asian tropical forests are vital sources of high-value timber and non-timber forest products (NTFPs). This study investigates the relationship between plant traits, wood density, and timber market value within the Dipterocarpaceae family, a critical contributor to the global tropical timber trade and a key structural component of many forests in Southeast Asia. Using a phylogenetic approach, we explored the correlation of morphological and life-history traits with timber price. Our results show that wood density is significantly associated with higher timber prices, and this relationship is strongly influenced by phylogenetic dependence. We found no evidence linking timber value to the conservation status of dipterocarp species, suggesting that economic exploitation does not necessarily correlate with species endangerment. These findings emphasize the importance of understanding the evolutionary patterns driving economically valuable traits in timber species, which can guide sustainable forest management and conservation strategies in Southeast Asia