50716 research outputs found
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Midwives' perceptions of support for new graduates:A survey that compared support from midwives who provide continuity of care with midwives from other models of care
No full textProblem: It is unknown if graduate midwives receive similar support from midwives providing continuity of care and midwives not working in continuity models. Background: All new graduate midwives require support as they transition from student to practitioner regardless of model of care in which they work. New graduate midwives are keen to work in continuity of care models but require good mentorship. Aim: To compare the perceptions of support provided by midwives to new graduates between those working in continuity of care models and those not working in those models. Methods: A cross-sectional study design with an online survey was undertaken. Quantitative analyses included descriptive statistics and independent t-tests. Content analysis was used for the open-ended questions data. Findings: Both groups of midwives reported it was important for new graduates to have knowledge and continue lifelong learning. Both groups of midwives also reported the importance of new graduates being involved in decision-making. Midwives working in continuity of care models were more likely to role model desirable behaviours of self-care, provide supportive environments, and think that new graduates should have more opportunities to work in continuity of care with a reduced workload than midwives not in continuity models. Conclusion: Midwives thought that it was important to listen to new graduates’ opinions and to value their opinions as an integral part of care. Mentoring and supporting new graduates with ongoing educational support and being inclusive is important for all regardless of model of care.</p
Fluctuating salinity during development impacts fish life histories
No full textClimate change and human activities are elevating the level and variability of salinity in freshwater ecosystems. Consequently, many aquatic species now experience more extreme developmental environments. Resultant shifts in developmental trajectories could change key life-history traits that persist into adulthood. The 'silver spoon' hypothesis posits that favourable developmental conditions lead to faster growth, earlier maturation and greater reproductive success. In contrast, the 'predictable adaptive response' hypothesis suggests that faster growth and earlier reproduction should be selected for under stressful developmental conditions because stress provides cues about a higher risk of mortality in future environments. To understand life-history responses to salinity during development, we reared a global pest, mosquitofish (Gambusia holbrooki), from birth in either freshwater control (0‰), stable-saline (10‰), or fluctuating-saline environments (0‰-20‰; mean = 10‰) until maturation. We then monitored their performance in early and late adulthood in a common garden setting. Fish in fluctuating salinity grew more slowly and had a reduced reproductive output (lower sperm count, smaller eggs) than those in stable elevated salinity. These differences are consistent with a more stable environment providing a 'silver spoon' effect. Conversely, fish in stable elevated salinity grew faster and matured earlier than those in freshwater, supporting a 'predictive adaptive response' whereby salinity is a stressor triggering faster development and accelerates reproduction. In addition, fluctuations in salinity altered the effect of higher salinity on self-maintenance. Stable elevated salinity caused a decrease in male telomere length and female gut length, but fluctuating salinity caused an increase in female gut length. Our results suggest that fluctuating versus stable salinity during development leads to distinct fish life histories. The effect sizes for some traits differed significantly between males and females, suggesting sex-specific responses to climate fluctuations.</p
Measuring and mitigating debugging effectiveness decay in code language models
No full textThe effectiveness of AI debugging follows a predictable exponential decay pattern; most models lose 60-80% of their debugging capability within just 2-3 attempts, despite iterative debugging being a critical capability for practical code generation systems. We introduce the Debugging Decay Index (DDI), a mathematical framework that quantifies when debugging becomes ineffective and predicts intervention points. Our strategic fresh start approach shifts from exploitation to exploration at strategic points in the debugging process, demonstrating that well-timed interventions can rescue the effectiveness of debugging. DDI reveals a fundamental limitation in current AI self-debugging and provides the first systematic metric to gauge LLM-based code generation.</p
Enhancing national digital resilience with digital commons
Get PDFThis stakeholder engagement tool summarises how governments can benefit from digital commons solutions: (1) Securing digital sovereignty, (2) Investing for the future, (3) Building trust. An online FAQ <https://dcpc.info/faq/> expands on these points and as well as proposing resources on procurement, firm support, products and services
Soft Robotic Sim2Real via Conditional Flow Matching
No full textModeling soft robots remains a significant challenge due to high computational costs and frequent mismatches with real-world behavior, a phenomenon known as the Sim2Real gap. This paper addresses the Sim2Real gap through conditional flow matching (CFM), which learns a mapping between the simulation domain and the real-world experimental domain. A neural network learns a conditional probability path that transforms simulated states into real-world observations, conditioned on control inputs, thereby minimizing simulation inaccuracies. The method is demonstrated through benchmark Sim2Sim and Sim2Real tensile tests, and additionally demonstrated in the domain of soft gripping using fin-ray grippers. A novel encoder architecture is introduced that learns a representation of the contact state, enabling the model to generalize to previously unseen interactions. The model provides a highly accurate prediction of force and deformation, successfully capturing complex elastic behaviors, including hysteresis and force fluctuations. Experimental results validate that CFM can bridge the Sim2Real gap for various soft robot morphologies, without requiring large datasets, and with strong generalization capabilities. Notably, the findings indicate substantial generalization capabilities in Sim2Real scenarios.</p
Forensic hair analysis. Why bother? Renewing perspectives on expertise amidst emerging AI technology – coming full circle
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