Open research Europe最新文献

Background: Silver (Ag) nanoparticles (NPs) are used increasingly in consumer and healthcare fabrics due to their antimicrobial properties. Abrasive leaching experiments have shown that AgNPs can be released during textile wear and cause a dermal exposure. Derived-no-effect-limit value for AgNPs ranges from 0.01 to 0.0375 mg/kg-body-weight, and thus, low exposures levels can cause relevant risk.

Methods: In this study AgNP release from textiles by artificial sweat immersion and mechanical stress was investigated. A mass balance model was used to calculate dermal Ag exposure and potential intake via percutaneous absorption and inadvertent (peri-)oral intake during wear of face mask, suit with a full body exposure and gloves. Mass flow analysis was performed for up to 8-h wear time and by using Ag penetration rate constants reported for fresh-, cryopreserved- and glycerolized skin grafts.

Results: Dermal intake risk characterization ratio (RCR) during 8-h wear time for glycerolized skin was up to 0.02 for face mask and 0.9 for full body wear in a worst-case condition. Wearing gloves for 1-h followed by single unintentional fingertip mouthing (contact area 11.5 cm ²) resulted in an RCR of 0.0002. RCR varied depending on the type of textile-product, exposure wear duration and skin type.

Conclusions: This study provides a comprehensive assessment of AgNPs release from textiles and their potential impact on human dermal exposure and was essential for understanding the safety implications for different exposure scenarios and mitigating potential risks.

Background: Bridges are vital construction infrastructures that almost every nation needs in order to function. Climate change is a significant issue, that especially affects the construction industry. It is very important that bridges are able to withstand the impacts of climate change and adaptation measures will be required to achieve this.

Methods: The digital twin will be developed using BIM to manage the climate change adaptation measures for the bridges. A 6D BIM model will be created that includes the 3D Revit model of a bridge featuring climate change measures, the climate change adaptation measures timeline schedule, climate change adaptation cost estimation, and carbon emission estimation, which will be produced using Revit software, Navisworks, and Granta EduPack. The results will show how 6D BIM can be used to support the adaptation of bridges to the effects of climate change.

Results: The findings underscore the efficacy of 6D BIM in enhancing bridge resilience against climate change impacts. The 3D model demonstrates integration of adaptation measures without compromising bridge functionality. Moreover, the 4D model's timeline scheduling facilitates hazard anticipation, project planning, communication enhancement, collaborative efforts, and project visualization. Cost estimations from the 5D model reveal varying costs among adaptation measures, while the 6D model highlights differences in carbon footprints. These BIM dimensions enable stakeholders to analyse effects on project costs and energy consumption, aiding sustainability and cost-efficiency considerations.

Conclusions: The study exhibits the literature review analysis, the risk assessment, research on climate change adaptation strategies, and implementation using Revit 2022, Navisworks 2022, and Granta EduPack software. By contributing to the adaptation of bridges to climate change effects, the research has provided valuable insights and practical implications for enhancing bridge resilience globally.

This paper presents the findings from a survey on factors influencing the adoption of agricultural Decision Support Systems (DSS). Our study focuses on examining the influence of behavioural, socioeconomic and farm specific characteristics on DSS adoption. Using two structural equation models, we investigate how these factors influence the willingness to pay (WTP) and willingness to adopt. Our analysis reveals nuanced insights into the user and farm-specific factors that influence the decision-making process of DSS adoption and WTP. Notably, farm size significantly influences both adoption and WTP, with larger farms more likely to adopt and exhibit higher WTP. To promote adoption, it is important to adapt promotion strategies, with a focus on productivity benefits for large-scale farms and addressing price barriers for smaller ones. Additionally, the main crop type grown impacts WTP and adoption, with arable crop farmers exhibit a lower WTP but more likely to adopt, especially in large-scale operations. Conversely, small-scale arable farmers exhibit higher WTP but lower adoption rates due to scale constraints. Farmer characteristics such as experience and attitudes also play a crucial role, with experienced users and those perceiving productivity improvements due to DSS showing higher WTP. In addition, adoption is also influenced by ease of use and pricing, underpinning the importance of user-friendly designs and clear cost justifications. DSSs with user-centric designs and clear cost justifications can enhance adoption rates.

Background: This study examines the scientific misinformation about climate change, in particular obstructionist strategies. The study aims to understand their impact on public perception and climate policy and emphasises the need for a systemic understanding that includes the financial, economic and political roots.

Methods: A systematic literature review (SLR) was conducted using the PRISMA 2020 model. The sample consisted of 75 articles published between 2019 and 2023, sourced from Web of Science, Scopus and Google Scholar. Methodological triangulation was performed to improve the analysis.

Results: The results show that technological approaches to misinformation detection, such as immunisation and fact-checking, are widely used. However, few studies look in depth at the operational structures that support systematic disinformation.

Conclusions: The study emphasises the urgent need to expand and deepen research on climate disinformation and argues for more global, comparative and adequately funded studies. It emphasises the importance of addressing the systemic complexity of disinformation and integrating different theoretical and methodological approaches. This will help to develop effective measures against hidden networks of influence and mitigate their disruptive effects. The research findings are relevant for policymakers, scientists, academics, the media and the public and will help to improve strategies to combat climate misinformation and promote science-based climate action.

The global antimicrobial resistance crisis has been the driver of several international strategies on antimicrobial stewardship. For their implementation at the field level, the veterinary sector encounters several specific challenges and in particular: (i) a shortage of experts in key disciplines related to antimicrobial stewardship, (ii) a lack of evidence-based antimicrobial treatment guidelines, and (iii) inferior diagnostic tests available compared to human medicine. The present white paper describes how the COST Action ENOVAT (the European Network for Optimization of Veterinary Antimicrobial Treatment, CA18217), comprising 332 persons from 51 countries, worked towards solutions to these challenges. Initially, surveys were conducted to explore the present state in Europe in terms of existing antimicrobial use guidelines and microbiology practices performed. Concurrently, various research activities were launched to optimize diagnostics, including development of epidemiological cut-offs, clinical breakpoints and matrix-assisted laser desorption ionization time of flight mass spectrometry interpretive criteria. Also, guidelines drafting groups working towards evidence-based antimicrobial treatment guidelines for six conditions in food-producing and companion animals were established. The processes and outcomes, also in terms of capacity building, are summarized in this white paper where emphasis is placed on sustainability of the activities. Although several ENOVAT initiatives and spin-off projects will continue beyond the Action, we recommend that a new European veterinary research agenda is launched focusing on research and funding leading to long-term impacts on veterinary antimicrobial use.

In recent years, deep learning has gained popularity for its ability to solve complex classification tasks. It provides increasingly better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance also bring efficiency problems, related to the storage of datasets and models, and to the waste of energy and time involved in both the training and inference processes. In this context, data reduction can help reduce energy consumption when training a deep learning model. In this paper, we present up to eight different methods to reduce the size of a tabular training dataset, and we develop a Python package to apply them. We also introduce a representativeness metric based on topology to measure the similarity between the reduced datasets and the full training dataset. Additionally, we develop a methodology to apply these data reduction methods to image datasets for object detection tasks. Finally, we experimentally compare how these data reduction methods affect the representativeness of the reduced dataset, the energy consumption and the predictive performance of the model.

Background: Complex policy problems are not amenable to simple solutions by a few powerful policy actors in one central government. They require collaboration across government and between actors inside and outside of government. However, this requirement for collaboration is no guarantee of collective action. Further, it is difficult to know how to collaborate effectively. We searched the academic and grey literature for advice on how to foster collaborative policymaking.

Methods: We conducted a qualitative systematic review (2024) of peer reviewed journal articles (Web of Science) and grey literature reports (Policy Commons). Each article or report had to inform advice on collaborative policymaking. We used an immersive and inductive approach to identify key themes and relate the results to well-established insights from policy theories.

Results: 86 texts meet the inclusion criteria (49 Web of Science, 37 Policy Commons). Most provide broad definitions of collaborative policymaking, which are similar to definitions of collaborative governance (and connected aims such as policy co-creation). Many assert or assume that greater collaboration, across and inside/outside of government, will improve policymaking and policy. Few individual studies give advice on how to collaborate effectively, but they combine to identify common features of collaboration.

Conclusions: We synthesise the available advice to identify five main features of collaborative policymaking: plan and prepare to collaborate, such as by designing rules and allocating resources; create a sense of collective purpose, such as by setting a boundary around the collaboration and co-producing a common vision; foster creative methods to visualise collaboration and design policy; create new forums to supplement formal collaboration; and clarify the roles and skills essential to each collaborative task.

Background: Floating photovoltaics (FPV) is an emerging technology that is gaining attention worldwide. However, little information is still available on its possible impacts in the aquatic ecosystems, as well as on the durability of its components. Therefore, this work intends to provide a contribution to this field, analysing possible obstacles that can compromise the performance of this technology, adding to an increase of its reliability and assessing possible impacts.The problem under study is related to the potential submersion of photovoltaic cables, that can lead to a degradation of its electrical insulation capabilities and, consequently, higher energy production losses and water contamination.

Methods: In the present study, the submersion of photovoltaic cables (with two different insulation materials) in freshwater and artificial seawater was tested, in order to replicate real life conditions, when FPV systems are located in reservoirs or in the marine environment. Electrical insulation tests were carried out weekly to assess possible cable degradation, the physical-chemical characteristics of the water were also periodically monitored, complemented by analysis to detect traces of copper and microplastics in the water.

Results: The results showed that the submersion of photovoltaic cables with rubber sheath in saltwater can lead to a cable accelerated degradation, with reduction of its electrical insulation and, consequently, copper release into the aquatic environment.

Conclusions: The test results pointed a probable relationship between submersion of cables with rubber outer shell and water freezing temperatures and the occurrence of accelerated degradation of the cable insulation layer. Reduced insulation resistance values were measured in this cable type after the occurrence of such temperatures, both in salt and freshwater, the cable presented visible exterior degradation signs. For this case copper residues were detected in the water.

Background: Surface topography has been shown to influence cell behavior and direct stromal cell differentiation into distinct lineages. Whereas this phenomenon has been verified in two-dimensional cultures, there is an urgent need for a thorough investigation of topography's role within a three-dimensional (3D) environment, as it better replicates the natural cellular environment.

Methods: A co-culture of Wharton's jelly-derived mesenchymal stem/stromal cells (WJ-MSCs) and human umbilical vein endothelial cells (HUVECs) was encapsulated in a 3D system consisting of a permselective liquefied environment containing freely dispersed spherical microparticles (spheres) or nanogrooved microdiscs (microdiscs). Microdiscs presenting 358 ± 23 nm grooves and 944 ± 49 nm ridges were produced via nanoimprinting of spherical polycaprolactone microparticles between water-soluble polyvinyl alcohol counter molds of nanogrooved templates. Spheres and microdiscs were cultured in vitro with umbilical cord-derived cells in a basal or osteogenic medium within liquefied capsules for 21 days.

Results: WJ-MSCs and HUVECs were successfully encapsulated within liquefied capsules containing spheres and microdiscs, ensuring high cellular viability. Results show an enhanced osteogenic differentiation in microdiscs compared to spheres, even in basal medium, evidenced by alkaline phosphatase activity and osteopontin expression.

Conclusions: This work suggests that the topographical features present in microdiscs induce the osteogenic differentiation of adhered WJ-MSCs along the contact guidance, without additional differentiation factors. The developed 3D bioencapsulation system comprising topographical features might be suitable for bone tissue engineering approaches with minimum in vitro manipulation.

Background: The aim of this study was to examine the test-retest reliability of the physical activity behavior, health and wellbeing questionnaire, in adolescent populations, administered by teachers in school settings, in the Republic of Ireland.

Methods: A cross-sectional, mixed sample of 55 participants (45.5% males: Age, 13.94 (±.40) years) were included. The participants completed the questionnaire on two occasions (T1 and T2), on the same day and time, one week apart following identical procedures. Variables for testing included physical activity behavior (n=13), health (n=11) and wellbeing (n=2). Test-retest reliability of the questionnaire's covariates, including family affluence and physical impairments were also examined.

Results: Systematic error (Bland-Altman plots) was found to be near to zero for each of the physical activity behavior, health and wellbeing variables. The combined mean coefficient of variation was lower for females (10.19%) in comparison to males (13.01%). The combined mean intraclass correlation coefficients were higher for females (0.901) than males (0.822). Similarly, the combined mean Cronbach alpha coefficient were higher for girls (0.908) than boys (0.821).

Conclusions: This study found the physical activity behavior, health and wellbeing questionnaire to be reliable for use in adolescent populations.