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Smokeless Tobacco and Oral Cancer in Global Perspective
Get PDFSmokeless Tobacco and Cancer in Global Perspective Despite use of smokeless tobacco products by more than 360 million people globally and increasing incidence of oral cancers, research and policies on these products have lagged behind those for cigarettes
Actionable information and climate change awareness drive consumer selection of environmentally beneficial garden plants
Get PDFDomestic gardens worldwide cover approximately 15β30Β % of residential urban space and with the appropriate plant composition, have potential to help manage urban water flows, regulate temperatures and air-pollution. However, the provision of these regulating ecosystem services depends upon the preferences and willingness of private garden owners to adopt environmentally beneficial planting, with associated traits that confer these benefits. This study tested whether the way information on beneficial plant traits is presented influences taxa choices. In an experimental online survey, participants were divided into two groups: one received only βsystem informationβ (basic facts about environmental problems: climate change, urban flooding, and poor air quality, nΒ =Β 208), while the other also received βaction-related informationβ (how to potentially address environmental problems by choosing plants with certain traits, nΒ =Β 211). Receiving βaction-related informationβ significantly influenced plant taxa selection; fewer choices were made for ornamental plants without traits that are beneficial for flood or pollution mitigation. Additionally, participants concerned about climate change were more willing to choose environmentally beneficial taxa, regardless of information group. These findings indicate that pro environmental planting choices in domestic gardens can be encouraged by providing actionable information and linking to existing climate change concerns
An investigation of the interaction and pH-activity relationship of ceria nanoparticles with a model membrane
Get PDFUsing rapid cyclic voltammetry (RCV) and an established electrochemical sensor, we clarify the interactions of in-house synthesised and well characterised cerium (IV) oxide (ceria) nanoparticles and nanocubes (NPs) with phospholipid membranes. The RCV results reveal that the interaction of ceria NPs with the phospholipid depends not only on the dispersion media and the particle coating, but also on the pH. No interaction was observed with the phospholipid layer when ceria NPs were dispersed in phosphate buffered saline (PBS) at pH 7.4 or in citric acid/citrate buffers (CCB) at pH values β₯4. However, a clear interaction was observed when ceria NPs were dispersed in acidic media. Notably, CeO2 NPs dispersed in either a glycine buffer at pH 3.0 (GLY 3.0) or CCB at pH 3.0, when in contact with the phospholipid monolayer, exhibited similar interaction behaviours to soluble Ce (+III) ions dispersed in the same media. However, both RCV responses also showed an increase in the background current (a semiconductor effect) indicative of the direct interaction of the nanoparticles with the uncoated electrode. In contrast, PBS-incubated ceria NPs did not produce an interaction with the phospholipid monolayer when dispersed in CCB 3.0 or GLY 3.0. Together, these findings suggest that the dispersion medium plays an important role in the interaction of CeO2 NPs either impeding or promoting interaction by changing the characteristics of the NP surface. In particular, we conclude that the interaction of ceria NPs with the phospholipid layer at acidic pH is facilitated by Ce(+III) ions or their complexes on the surface of the nanoparticles
EventBox: A novel visual encoding for interactive analysis of temporal and multivariate attributes in event sequences
Get PDFThe rapid growth and availability of event sequence data across domains requires effective analysis and exploration methods to facilitate decision-making. Visual analytics combines computational techniques with interactive visualizations, enabling the identification of patterns, anomalies, and attribute interactions. However, existing approaches frequently overlook the interplay between temporal and multivariate attributes. We introduce EventBox, a novel data representation and visual encoding approach for analyzing groups of events and their multivariate attributes. We have integrated EventBox into Sequen-C, a visual analytics system for the analysis of event sequences. To enable the agile creation of EventBoxes in Sequen-C, we have added user-driven transformations, including alignment, sorting, substitution and aggregation. To enhance analytical depth, we incorporate automatically generated statistical analyses, providing additional insight into the significance of attribute interactions. We evaluated our approach involving 21 participants (3 domain experts, 18 novice data analysts). We used the ICE-T framework to assess visualization value, user performance metrics completing a series of tasks, and interactive sessions with domain experts. We also present three case studies with real-world healthcare data demonstrating how EventBox and its integration into Sequen-C reveal meaningful patterns, anomalies, and insights. These results demonstrate that our work advances visual analytics by providing a flexible solution for exploring temporal and multivariate attributes in event sequences
Leveraging large language models for thematic analysis: a case study in the charity sector
Get PDFThis study explores how large language models (LLMs) can support deductive and inductive thematic coding in real-life contexts, balancing AI-driven efficiency with essential human oversight. Using three datasets from Tearfund, a UK-based Christian charity, we propose a dual-role humanβLLM collaborative framework where the LLM functions as an initial annotator and a validator. In the deductive phase, GPT-4o and GPT-4o-mini were compared against human coders. GPT-4o achieved a substantial agreement in multi-label thematic categorization (ΞΊβ=β0.61β0.65), while GPT-4o-mini showed a moderate agreement (ΞΊβ=β0.41β0.58). Both models excelled in sentiment analysis (ΞΊβ=β0.91β0.95), but struggled with evaluating evidence of impact due to contextual complexity (ΞΊββ€β0.01). GPT-4o-mini exhibited greater output variability and instability than GPT-4o, but benefited more from few-shot learning to mitigate hallucinations. In the inductive phase, GPT-4o demonstrated a strong semantic alignment with human-generated themes (cosine similarityβ=β0.76β0.79) though its tendency toward broad themes required human refinement. Despite their potential to streamline thematic analysis, LLMs also pose limitations and implementation challenges, including inconsistencies in excerpt extraction (precisionβ=β0.41, recallβ=β0.53) and the trade-off between the time saved in coding and the time required for human validation. To facilitate practical implementation, we provide reusable prompt templates for four stages: context, instructions, data processing, and verification. Our findings underline the indispensable role of human expertiseβfrom prompt engineering and managing hallucinations to final verificationβto ensure accurate and trustworthy AI-assisted analyses. While LLMs can enhance qualitative analysis, their full potential is only realized under skilled human guidance
A brief history of (controversial) extrajudicial communication
No full textThis chapter presents a historical survey of controversial extrajudicial discourse in the area in which it arguably comes under greatest scrutiny, namely, discourse that relates to the political views, activities, and affiliation of judges. The chapter focuses on six judges who held office between the early modern period and the present day - Ellesmere, Coke, Hardwicke, Diplock, Denning, and Dilhorne - all of whom were active in extra-judicial discourse on politically sensitive or contested issues. It uses published and unpublished records of their activity to analyse how and why they engaged extrajudicially on controversial matters. I suggest that their extrajudicial communication falls into three broad types of activity which I term lobbying, evangelising, and problem-solving. The chapter concludes by arguing that the nature of the judicial role in the UK makes extrajudicial communication inevitable, including on controversial matters. History demonstrates that while this can raise genuine concerns, it can also be fundamental to the effective development of the legal system. The task for law is to manage the tensions created by the judiciaryβs extrajudicial role, rather than seeking to avoid it altogether
How people decide to consume (more) alcohol when feeling stressed
No full textBackground and Aims: The tension reduction hypothesis suggests that people consume alcohol to alleviate stress. While previous studies showed stress increases alcohol's absolute value,
alcohol's value relative to alternatives should be more relevant for drinking decisions. We aimed to test whether acute stress causes individuals to choose alcohol over appealing non-alcoholic
alternatives and to identify the cognitive mechanisms underlying this choice behavior.
Design: Laboratory-based randomized 2Γ2 experimental study.
Setting: Controlled laboratory environment including a simulated bar setting.
Participants: 160 adults (56% male; mean age=31 years; median AUDIT=8) who regularly consume alcohol.
Interventions: Participants first rated beverages and made repeated choices between alcoholic and non-alcoholic options. They then received either alcoholic beverages (target BrAC=.06%) or nonalcoholic beverages, followed by either a personalized stress induction using autobiographical emotional memories or a neutral control procedure.
Measurements: Primary outcomes were proportion of choices for alcoholic beverages and decision response times. Choice behavior was analyzed using drift diffusion modeling to decompose decisions into three mechanisms: decision carefulness (boundary parameter), sensitivity to prior preferences (drift rate), and bias toward alcohol regardless of preference (bias parameter).
Findings: Stress moderately increased choices for alcohol (95% HDI [0.01, 0.13]), but only in sober participants. Drift diffusion modeling revealed that stress primarily affected decisionmaking by inducing a bias toward alcohol during evidence accumulation (95% HDI [0.19, 0.76]), without impacting decision carefulness or evidence sensitivity. This computational bias was stronger than observed in raw choice behavior, indicating that while stress consistently biases
evaluation toward alcohol, this bias only sometimes overcomes competing considerations (i.e., a person might reverse a preference from 'a little bit' to 'not really', but not from 'a little bit' to 'not at all').
Conclusions: Our results are relevant to adults who report risky drinking without alcohol treatment history or comorbidity and provide support for the tension reduction hypothesis by demonstrating that stress can occasionally lead individuals to choose alcohol even when they prefer the nonalcoholic alternative. However, this effect only appeared in sober participants who have not yet consumed any alcohol, suggesting the hypothesis primarily explains decisions about initiating rather than continuing drinking episodes
Adhesive layer formation and its dual role in tribological performance and surface integrity of Ti-6Al-4V: implications for the machining process
Get PDFThe poor machinability of Ti-6Al-4V (Ti64), characterized by adhesive and abrasive wear, low thermal conductivity, and high chemical reactivity, continues to hinder efficient manufacturing. Among these challenges, adhesive layer formation on tool flank faces remains poorly understood despite its critical influence on tool degradation and workpiece surface integrity. To address this, this study investigates the tribological behaviour of WC/Co-Ti64 pin-on-disc sliding contacts under dry and minimum quantity lubrication (MQL) conditions through both experimental and numerical approaches. Experimental results show that thick, stable, and intact adhesive layers transferred from Ti64 discs was formed on WC/Co pin surfaces under dry and low MQL flowrate conditions. These layers are associated with reduced friction coefficients and lower disc wear but simultaneously contribute to compromised surface integrity. Comparative boundary element method (BEM) simulations with 316 L stainless steel reveal that the lower elastic modulus of Ti64 adhesive layers significantly reduces nominal contact pressure and subsurface von Mises stress, lowering friction coefficients and enhancing mechanical stability of adhesive layer. However, the accompanying increase in surface roughness intensifies local stress concentrations and result in thicker work-hardened layers on Ti64 disc, which align well with BEM simulation results. Conversely, high MQL flowrate inhibited adhesive layer formation, leading to higher friction and wear but producing smoother surfaces and thinner work-hardened layer. The findings offer new mechanistic insights into complex interplay between adhesive layer, lubrication and surface topography, and present the first direct evidence of the dual role of adhesive layer: reducing friction and tool-side wear but compromising workpiece surface integrity
Transform(AI)ng Radiology with CheXSBT: Integrating Dual-Attention Swin Transformer with BERT for Seamless Chest X-Ray Report Generation
Get PDFRadiology 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 radiologistsβ 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
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