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Consensus Research Priorities for Cancer Nursing in Europe:A Delphi Study
Get PDFPurposeIdentifying priority areas for cancer nursing research is essential for aligning research efforts with regional needs and improving outcomes for people affected by cancer. This Delphi study aimed to develop a strategic agenda of cancer nursing research priorities relevant to the European context.MethodA panel of experts in cancer nursing practice, research, policy, and education participated in a three-round Delphi study. A total of 115 research topics across 11 thematic areas were pre-identified through a systematic review and consultation with cancer nurses. From September 2023 to March 2024, participants ranked the importance of each topic. Final prioritisation was based on weighted average rankings and an 80% consensus threshold in round 3.ResultsEighty-one cancer nursing experts participated in at least one round, with 57 completing all three rounds. In round 3, consensus was achieved for 73 of 123 items (59.3%). The three highest-ranked thematic areas for future research were: 1) nurse-led care and models of cancer care, 2) unmet needs in cancer care, and 3) education, training, and professional development for nurses. The top three specific research priorities were: 1) understanding factors influencing recruitment, retention, and turnover within the European oncology nursing workforce, 2) evaluating the effectiveness of nurse-led interventions for people living with cancer, and 3) understanding risk factors for cancer diagnosis.ConclusionsThis study presents a consensus-based, regionally grounded research agenda for cancer nursing in Europe. The findings address urgent challenges in workforce sustainability, care models, and prevention, with implications for improving patient outcomes and guiding future nursing research
Acoustic and Mechanical Performance of Treated Rubber–Concrete Composites for Soundproofing in Wind Power Applications
Get PDFThe current study examines the innovative use of rubber–concrete composites as structural solutions that provide significantly higher noise absorption properties compared to traditional concrete. Focusing on their potential for sound insulation in challenging environments such as wind energy infrastructure, the study examines the effect of varying contents of ground tyre rubber (GTR) content (20%, 40%, and 60% by volume) and acetone treatment duration (0, 1, 6, and 24 h) on the characteristics of the composite. The results demonstrate that these rubber–concrete composites significantly improve both sound absorption and sound insulation. An increase in sound absorption coefficients to approximately 0.18 was observed, representing an average improvement of 43.4% compared to the average coefficient of the reference mixture, 0.043. This improvement is particularly effective in the 100–1250 Hz frequency range and maintains stable properties from 50 to 1600 Hz. Sound transmission losses also showed a clear improvement in the mid-frequency ranges. Despite their excellent acoustic characteristics, these structural composites demonstrate a compromise in mechanical properties. Compressive strength decreased from approximately 43–46 MPa (control) to 25–38 MPa at 60% rubber content after 28 days, representing a 40–46% reduction. The reduction in flexural strength was even more pronounced, decreasing by approximately 60% at a rubber content of 35%. However, treatment of GTR with acetone significantly improved interfacial bonding, increasing mechanical integrity at moderate rubber doses (20–40%). The optimal range of rubber content, providing a balance between acoustic benefits and structural integrity, appears to be 15–25%
Electoral systems and geographically targeted oversight:Evidence from the Taiwan Legislative Yuan
Get PDFElectoral systems have profound effects on legislator-constituent communication and representation. In this paper, we examine how Taiwan’s electoral reform—from multi-member districts under the Single Non-Transferable Vote (SNTV) to single-member districts under a Mixed Member Majoritarian system (MMM)—shapes district legislators’ particularistic behavior. Using fine-tuned transformer architectures, we analyze over 63,000 parliamentary questions from 402 district legislators spanning two decades to identify geographically targeted content. Controlling for legislator and municipal characteristics, we find that the reform from SNTV to MMM reduces geographically targeted questions, though this effect varies across municipalities with different economic profiles. Our analysis reveals that SNTV is associated with greater particularistic responsiveness to local socioeconomic conditions than single-member districts under MMM, suggesting that candidate-centered electoral systems of different types produce different behavioral incentives
Multiple oceanic sources of alkylamines in Southern Ocean atmospheres
Get PDFMeasurements of pre-industrial conditions are of paramount importance for understanding historical climate change. The Southern Ocean and Antarctic continent are some of the least polluted environments on planet Earth. Alkylamines can rapidly partition into aerosols, increasing their mass, as well as form new particles altogether. We demonstrate the importance of pelagic “open ocean” (OO) and sympagic “sea ice” (SI) regions in supplying distinct organic nitrogen aerosol components. In the aerosol phase, dimethylamine (DMA) and trimethylamine (TMA) are both secondary, though DMA likely originates mainly from pelagic regions, while TMA is associated mainly with sympagic regions. Parallel measurements in ice and surface waters reveal that melting sea ice contains a factor of four more TMA than coastal Antarctic Peninsula waters; and seventeen times more TMA than OO regions - suggesting additional coastal Antarctic sources. To better interpret future climate change, we recommend employing regional atmospheric chemistry models to understand these diverse aerosol sources
Beyond Answers:Pedagogical Design Rationale for Multi-Persona AI Tutors
Get PDFThis paper reports a design-rationale account of building and deploying a small ecosystem of AI-driven educational conversational agents with distinct pedagogical personas. Two strands target school contexts: (i) Talk to Bill, a historically grounded Shakespeare interlocutor intended to support close reading, contextual understanding, and interpretive dialogue; and (ii) Here to Help, a set of UK GCSE subject- and exam-board-specific tutors designed for formative practice in recognised question formats with feedback and iterative improvement. The third strand comprises six complementary assistants for an undergraduate Human–Computer Interaction (HCI) module, each bounded to a workflow-aligned role (e.g., empathise-stage coaching, study planning, course operations), with guardrails to privilege process quality over answer generation. We describe how persona differentiation is mapped to established learning, engagement, and motivation theories; how retrieval-augmented generation and provenance cues are used to reduce hallucination risk; and what early deployment observations suggest about orchestration, integration, and incentives. The contribution is a transferable, auditable rationale linking theory to concrete dialogue and UI moves for multi-persona tutoring ecosystems, rather than a claim of causal learning gains
Misspecified models create the appearance of adaptive control during value-based choice
Get PDFDecision scientists have grown increasingly interested in how people adaptively control their decision making, exploring how metacognitive factors influence how people accumulate evidence and commit to a choice. A recent study proposed a novel form of such adaptive control, whereby the values of one's options contribute to both the formation of a decision and the effortful invigoration of a response. In this framework, the control process was operationalized in a drift diffusion model as the lowering of the decision threshold on difficult trials. Reanalyzing the data from this experiment, we establish alternative explanations for these findings. We show that the reported evidence for controlled threshold adjustments can be explained away by task confounds, time-dependent collapses in decision thresholds, and stimulus-driven dynamics in an alternative form of evidence accumulation. Our findings challenge the specific evidence for this new theory of motivated control while at the same time revealing paths and pitfalls in computational approaches to a more general understanding when and how control guides decision-making.</p
Ernest‑Charles Babut’s Saint Martin de Tours:The Fourth Century in the Third Republic
Get PDFThis article examines the work of the French historian and war hero Ernest-Charles Babut (1875–1916), and in particular his scholarship on fourth-century Christianity, especially that on the figure of St Martin of Tours. It is suggested that much of his discussion of the period, notably that of church/state relations and the question of compulsory military service, may profitably be read as commentary on the issues in French public life in the late nineteenth and early twentieth centuries
Machine learning approach for the prediction and optimization of heat transfer performance of ZnO/TiO<sub>2</sub>-R123 in ORC evaporator
No full textThis study employs machine learning approaches to develop a back propagation (BP) model for capturing flow boiling heat transfer characteristics of ZnO/TiO2-R123 in a horizontal tube and conducts a bi-objective optimization considering heat transfer performance and flow resistance simultaneously. The BP neural network model is established with 750 groups of experimental data as training samples and 150 groups of experimental data as testing samples. The training accuracy and predictive accuracy of the BP model are analysed in detail. The effects of six operation parameters on heat transfer coefficient and pressure drop are examined, along with a bi-objective optimization conducted to maximize heat transfer coefficient and minimize pressure drop. The results indicate that the BP neural network model achieves a very high prediction accuracy, with a relative error of ±1.5 % for the prediction of heat transfer coefficient pressure drop. The heat transfer coefficient is negatively correlated with the vapor quality and increases slowly with the mass flux. The pressure drop increases slightly with the outlet temperature and decreases slowly with the outlet pressure at first, then gradually becomes steeper. The optimal solution for heat transfer coefficient and pressure drop are 4500 W/(m2·K) and 0.022 MPa, respectively.</p
Traceability and Transparency in Global Food Value Chains
No full textThe globalization of food value chains has intensified the need for robust traceability and transparency systems to safeguard food safety, enhance consumer trust, and ensure sust ainability. This chapter examines the conceptual foundations, technological enablers, regulatory frameworks, and practical challenges shaping traceability and transparency across agriculture, manufacturing, and distribution. Traceability is defined as the ability to track food products and inputs across the entire value chain, while transparency emphasizes making this information accessible and verifiable to stakeholders. Together, they form the backbone of accountability in global food systems. Technological advances—such as blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI)—are transforming how food products are monitored, recorded, and shared, enabling predictive analytics, real-time data collection, and automated compliance. Case studies, including Walmart’s blockchain partnership with IBM, demonstrate the benefits of improved safety, fraud reduction, and operational efficiency, while also highlighting barriers such as cost, scalability, and data privacy. International standards (e.g., ISO 22005, Codex Alimentarius, and HACCP guidelines) provide regulatory guidance but face challenges in harmonization across diverse jurisdictions. Despite technological promise, implementation is hindered by economic disparities, interoperability issues, and limited capacity among small and medium-sized enterprises. The chapter argues that future food value chains will be increasingly digitalized, data-driven, and collaborative, requiring vertical and horizontal integration across stakeholders. Ultimately, embedding traceability and transparency at the core of food systems is essential to strengthen resilience, enhance sustainability, and build consumer confidence in a complex global food economy
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