Desensitisation to cigarette package graphic health warnings:a cohort comparison between London and Singapore

OBJECTIVES: We compared 2 sociocultural cohorts with different duration of exposure to graphic health warning labels (GHWL), to investigate a possible desensitisation to their use. We further studied how a differing awareness and emotional impact of smoking-associated risks could be used to prevent this. SETTING: Structured interviews of patients from the general respiratory department were undertaken between 2012 and 2013 in 2 tertiary hospitals in Singapore and London. PARTICIPANTS: 266 participants were studied, 163 Londoners (35% smokers, 54% male, age 52±18 years) and 103 Singaporeans (53% smokers, p=0.003; 78% male, p<0.001; age 58±15 years, p=0.012). MAIN OUTCOMES AND MEASURES: 50 items assessed demographics, smoking history, knowledge and the deterring impact of smoking-associated risks. After showing 10 GHWL, the impact on emotional response, cognitive processing and intended smoking behaviour was recorded. RESULTS: Singaporeans scored lower than the Londoners across all label processing constructs, and this was consistent for the smoking and non-smoking groups. Londoners experienced more ‘disgust’ and felt GHWL were more effective at preventing initiation of, or quitting, smoking. Singaporeans had a lower awareness of lung cancer (82% vs 96%, p<0.001), despite ranking it as the most deterring consequence of smoking. Overall, ‘blindness’ was the least known potential risk (28%), despite being ranked as more deterring than ‘stroke’ and ‘oral cancer’ in all participants. CONCLUSIONS: The length of exposure to GHWL impacts on the effectiveness. However, acknowledging the different levels of awareness and emotional impact of smoking-associated risks within different sociocultural cohorts could be used to maintain their impact

Evaluation of outcomes of a formative objective structured clinical examination for second-year UK medical students

OBJECTIVES: To explore how formative OSCEs influence student performance and perception when undertaking summative OSCEs. METHODS: We introduced formative OSCEs for second-year medical students at a large London medical school. Examination data from both formative and subsequent summative OSCEs were analysed to determine the effect on summative OSCE performance. We gathered student perceptions using an anonymous online survey tool. The data was investigated using a standard scale of 1 to 5 and qualitative analysis of free text. RESULTS: Overall, 46.6% and 85.0% of students passed the formative and summative OSCEs respectively. Formative OSCEs did not improve overall pass rates in summative OSCEs. Inclusion of an individual formative station was associated with improved performance in that station in summative OSCEs, with one exception. Formative OSCEs had a positive predictive value of 92.5% for passing the summative OSCE but limited negative predictive value. Students who passed fewer than two out of three formative OSCE stations were significantly more likely to fail the summative OSCE (78.2% vs 89.7%, p <0.001). Students felt formative OSCEs were good exam preparation and suggested logistical changes. CONCLUSION: Formative OSCEs were associated with improved performance in subsequent summative OSCEs only for identical stations. They did not improve overall pass rates in summative OSCEs, and did not predict performance well. Students viewed the formative OSCE as a positive and useful activity. However, to maximise its benefit as a tool for learning, students need better communication about the role and purpose of formative OSCEs

IEEE Security & Privacy Magazine

The memory-safe CHERI C and C++ languages build on architectural capabilities in the CHERI protection model. With the development of two industrial CHERI-enabled processors over the past two years, Arm’s Morello and Microsoft’s CHERIoT, CHERI may offer the fastest path to widely deployed memory safety.Additional support was received from Arm, Google, and Microsoft

Deep Underground Neutrino Experiment (DUNE) Near Detector Conceptual Design Report

International audienceThe Deep Underground Neutrino Experiment (DUNE) is an international, world-class experiment aimed at exploring fundamental questions about the universe that are at the forefront of astrophysics and particle physics research. DUNE will study questions pertaining to the preponderance of matter over antimatter in the early universe, the dynamics of supernovae, the subtleties of neutrino interaction physics, and a number of beyond the Standard Model topics accessible in a powerful neutrino beam. A critical component of the DUNE physics program involves the study of changes in a powerful beam of neutrinos, i.e., neutrino oscillations, as the neutrinos propagate a long distance. The experiment consists of a near detector, sited close to the source of the beam, and a far detector, sited along the beam at a large distance. This document, the DUNE Near Detector Conceptual Design Report (CDR), describes the design of the DUNE near detector and the science program that drives the design and technology choices. The goals and requirements underlying the design, along with projected performance are given. It serves as a starting point for a more detailed design that will be described in future documents

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation