MNM Consulting (France)

Although student evaluations of teaching (SET) in higher education have recently gained considerable interest, little is known about whether course level influences these evaluations. Accordingly, two datasets, obtained from a large public university in the U.S. Midwest, were analyzed to investigate whether course level makes a difference in SET. The first dataset included 25,306 evaluations across eight course levels collected using the SET questionnaire. A one-way multivariate analysis of variance (MANOVA), followed by univariate analyses of variance (ANOVAs), was conducted to test whether course level makes a difference in SET scores. To cross-validate the results and ensure generalizability, the same analyses were conducted using a second, smaller dataset (N = 444). MANOVA results revealed a statistically significant effect for course level on the combined SET dimensions across both campuses. All univariate ANOVAs were also significant across both campuses. Follow-up post hoc comparisons, with level 1 as the reference group, were statistically significant, especially for level 8. Overall, these results underscore the importance of accounting for course level when interpreting SET and encourage researchers to include key covariates (e.g., class size, discipline, instructor experience, and student composition) to identify the factors driving course-level differences.

In the era of digital transformation, industries such as healthcare, manufacturing, and government are increasingly challenged to manage complex processes efficiently. Traditional coding approaches often fail to meet business requirements promptly, leading to a shift towards low-code platforms for rapid application delivery. This paper presents a novel approach to decomposing and distributing the execution of IoT edge processes based on device capabilities using Node-RED, a visual programming tool. We introduce process clustering, containerization, and deployment orchestration to optimize IoT workflows. Our method leverages Distributed Node-RED (DNR) and container technologies like Kubernetes (k3s) to enable dynamic edge device registration and capability sharing. A fleet management use case demonstrates the cost-efficiency of our approach, highlighting significant savings in message transmission costs. The proposed method ensures agility and flexibility in IoT process management, paving the way for more efficient digital transformation. Future work will focus on ensuring process equivalence and deadlock freeness in distributed systems.

IntroductionPostpartum haemorrhage (PPH) remains the leading cause of maternal death. Yet there is a lack of clarity around what research is needed to determine what works and how best to deliver proven PPH interventions. This article describes a WHO-led effort to develop a global PPH research agenda for 2023–2030, to reinvigorate research and innovation while avoiding duplication and waste.MethodsPotential questions were culled from evidence gaps in a forthcoming Lancet PPH series, a pipeline analysis on PPH medicines and devices, international PPH guidelines, previous research prioritisation efforts and submissions from a reference group of PPH experts and stakeholders. Questions were deduplicated and consolidated, categorised into three tracks (innovation, implementation and cross-cutting) and subjected to an online prioritisation survey. Survey participants (n=120) assessed these questions using five criteria (answerability, effectiveness, deliverability, maximum potential for disease burden reduction and equity) following the Child Health and Nutrition Research Initiative methodology. The outcome of this exercise was complemented by an in-person consensus meeting (Global PPH Summit from 7 March 2023 to 10 March 2023 in Dubai, United Arab Emirates) to finalise the research agenda.ResultsFifteen research questions (five per track) were identified as top priority. The top question per track called for research on the comparative effectiveness and safety of alternative routes of administration (other than the intravenous route) of tranexamic acid in the treatment of PPH (innovation); identifying barriers and facilitators affecting the adoption and use of evidence-based recommendations for PPH management (implementation) and the effectiveness of a strategy of early detection and first response treatment using a bundle of recommended interventions for improving PPH-related outcomes (cross-cutting).ConclusionThis shared research agenda should guide future investments into PPH studies with high potential to transform policy and clinical practice in the near term to medium term. Funding for the new research priorities is urgently needed.

Background: Self-amplifying mRNA vaccines have the potential to increase the magnitude and duration of protection against COVID-19 by boosting neutralizing antibody titers and cellular responses. Methods: In this study, we used the immunogenicity data from a phase 3 randomized trial comparing the immunogenicity of ARCT-154, a self-amplifying mRNA COVID-19 vaccine, with BNT162b2 mRNA COVID-19 vaccine to estimate the relative vaccine efficacy (rVE) of the two vaccines over time in younger (<60 years) and older (≥60 years) adults. Results: By day 181 post-vaccination, the rVE against symptomatic and severe Wuhan-Hu-1 disease was 9.2-11.0% and 1.2-1.5%, respectively, across age groups whereas the rVE against symptomatic and severe Omicron BA.4/5 disease was 26.8-48.0% and 5.2-9.3%, respectively, across age groups. Sensitivity analysis showed that varying the threshold titer for 50% protection against severe disease up to 10% of convalescent sera revealed incremental benefits of ARCT-154 over BNT162b2, with an rVE of up to 28.0% against Omicron BA.4/5 in adults aged ≥60 year. Conclusions: Overall, the results of this study indicate that ARCT-154 elicits broader and more durable immunogenicity against SARS-CoV-2, translating to enhanced disease protection, particularly for older adults against Omicron BA.4/5.