Research Institute for Knowledge Systems

The lack of practical knowledge and effective engagement with gender in blue carbon initiatives poses a threat to their success and sustainability and risks exacerbating existing gender inequalities. Developed by an interdisciplinary team of experts, this paper proposes a research agenda, comprised of seven research areas as well as priority actions, designed to catalyze a shift towards more just, equitable and effective blue carbon initiatives. This paper is a call-to-action to increase commitment, funding, and collaborative, interdisciplinary science to close the gender gap in the conservation and restoration of blue carbon ecosystems and enhance their social-enovironmental impacts.

IntroductionSeveral barriers, such as lack of time, knowledge and support, hinder clinicians from providing an individually tailored physical activity (PA) prescription and referral to their patients. As a result, “exercise is medicine” (E = M) is not systematically implemented in clinical care today. Many studies have identified facilitators and barriers to implementation, yet linking these factors to tailored implementation strategies is still an under-researched area. Therefore, this study aimed to apply Implementation Mapping to develop an implementation protocol to support the individually tailored PA prescription in hospital care.MethodsWe used strong stakeholder participation and, we applied the five tasks of the systematic Implementation Mapping approach to match implementation strategies to implementation barriers and facilitators identified through interviews with clinicians working at two university hospitals in the Netherlands.ResultsWe identified clinicians as primary actors. Secondary actors were managers of the departments and stakeholders in the broader context. For each actor group, performance objectives were defined. We matched previously identified facilitators and barriers to theory and evidence-informed implementation strategies from the Effective Practice and Organisation of Care taxonomy using the CFIR Strategy Matching Tool. Next, we translated these implementation strategies (e.g., active learning, audit, and feedback, technical assistance, peer education) into practical activities to support the implementation of the E = M tool, such as training for clinicians, creating overviews of possible local exercise referral options, and appointing role models for clinicians. Lastly, these activities were bundled into an implementation protocol. The implementation protocol consisted of a set of implementation activities to support and guide clinicians during the adoption, implementation, and sustainability process of the prescription of E = M. All activities were supported by implementation tools, practical applications, and materials while allowing tailoring to the specific clinical context.Discussion/conclusionThis study illustrates the application of Implementation Mapping to design an implementation protocol to support and guide the prescription of E = M by clinicians in the hospital environment, using strong stakeholder participation in the development process. The stepwise development of the implementation protocol can serve as an example for researchers or practitioners preparing for E = M implementation.

Transport pipelines are critical for the distribution of energy commodities, refined products and essential materials like water and hydrogen.While often buried underground for protection from environmental factors, they remain vulnerable to distant threats such as intentional or accidental explosions.This study investigates the mechanical response of steel pipelines to seismic excitation from nearby buried explosive charges.An experimental approach was employed to measure the stresses and deformation of a buried, straight steel pipeline segment using high-frequency strain gauges attached to its surface before burial in damp, sandy soil.The gauges were strategically placed to capture various deformation modes, such as ovalisation and axial strain.Seismic loading was applied using buried plastic explosive charges weighing up to 16 kg TNT-equivalent, detonated at distances up to 8 m from the pipeline centre.Seismic ground shock characteristics were evaluated using triaxial MEMS accelerometers.The results establish a relationship between the explosion intensity, measured ground shock characteristics (e.g., peak particle velocity) and pipeline deformation.For the experimental scenarios, pipeline deformation remained within the elastic limit, with maximum strains measured in the order of 100 -200 10 -6 .The ground shock was found to be significantly damped between the detonation point and the pipeline.Thus, when no other loads are present, transport pipelines buried in dry, sandy soil are predicted to deform elastically for subsoil detonations up to 100 kg TNT-equivalent at a 14.5-m distance.

SummaryBackgroundSurgical site infection (SSI) is the most common postoperative complication. Goal-directed haemodynamic therapy (GDHT) may help to prevent SSI, but recommendations for its use initially have been set at conditional because of low-certainty evidence at the time. An updated systematic review with SSI as the primary endpoint has not been performed since 2011, and important new evidence has emerged. We assessed the influence of GDHT on SSI and other postoperative outcomes.MethodsWe searched Ovid/MEDLINE, Excerpta Medica Database (Embase.com), and Cochrane library from inception up to September 2024 for randomised controlled trials comparing the effect of any GDHT algorithm to conventional fluid therapy on SSI incidence in adult patients undergoing surgery and analysed eligible data using random effects. We conducted several subgroup analyses, including the risk of bias (RoB), and a trial sequential analysis (TSA). We evaluated the certainty of evidence using Grading of Recommendations, Assessment, Development, and Evaluations. This study is registered with PROSPERO, CRD42022277535.FindingsWe found 75 studies that met the inclusion criteria with an incidence of 1,478 SSI among 13,010 patients (11.4%). The incidence of SSI was reduced from 13.3% in the conventional fluid therapy to 9.4% after GDHT (absolute risk reduction 3.9%); pooled relative risk 0.71 (95% CI 0.62–0.81). Subgroup analysis for the low RoB studies revealed comparable results. Meta-regression indicated no strong evidence for individual subgroup effects. In the TSA, the cumulative z-line crossed the boundary for effect.InterpretationHigh-certainty evidence indicates that GDHT reduces the risk of SSI when compared to conventional fluid therapy in adults undergoing surgery. New studies are unlikely to change this outcome. These findings justify a stronger recommendation for the use of GDHT.FundingDutch Association for Quality Funds Medical Specialists.

Predicting the probability that a given location will be burnt by a wildfire is an important part of understanding the risk that wildfires pose and how our management actions (e.g., prescribed burning) can reduce this risk. Existing methods to quantify this burn probability involve simulating the spread of many thousands of individual wildfires, making them highly computationally expensive. To reduce this expense, we propose strategies that enable the development of computationally efficient machine learning assisted metamodels for estimating burn probability, which are demonstrated for a case study in South Australia. Artificial neural networks are used as the metamodel to emulate the outputs of a landscape fire simulation model. Development of the metamodel is facilitated by reducing the input and output dimensionality of the simulation model by a factor of 10,000–1,000,000, while still being able to predict burn probabilities with high accuracy (approximately ± 7.4% error, on average) and only requiring 0.6% of the computational time compared with an approach using landscape fire simulation models. This opens the door to obtaining many thousands of spatially distributed estimates of burn probability, as is required when optimising fuel treatment strategies.

Youth engagement in social entrepreneurship presents a powerful avenue for addressing contemporary global challenges and fostering sustainable development. This research explores the motivations, challenges, and opportunities shaping the involvement of young individuals in social ventures, with a focus on understanding the role of support mechanisms in facilitating their success. Adopting a mixed-methods approach, we conducted a quantitative survey among 50 young social entrepreneurs, followed by in-depth interviews with 30 participants. Our findings reveal that youth are primarily driven by a desire to create positive social impact, passion for addressing societal challenges, and belief in the potential of entrepreneurship to drive change. However, they face significant challenges such as limited access to funding and regulatory barriers, which vary across socio-economic contexts. Support mechanisms including mentorship, capacity building programs, and access to funding play crucial roles in enhancing the success and sustainability of youth-led social ventures. By bridging gaps in existing literature and offering empirical insights, this research contributes to the discourse on youth empowerment and social entrepreneurship, providing evidence-based recommendations for stakeholders across sectors.

Wildfires can be dangerous phenomena, creating risks for communities that are likely to be exposed to wildfire. The likelihood of community exposure to a wildfire is influenced by the interaction of fire behaviour factors (weather, fuel and topography) across multiple spatial scales.Our objective is to develop an index that measures the connectivity of our communities to the multi-scaled interactions of fire behaviour factors in a computationally efficient manner. The index serves as a proxy for relative wildfire likelihood and represents temporally and spatially variable patterns in wildfire likelihood. The index will support wildfire risk assessments, including exploring problems such as optimising landscape treatment placements.Here, we introduce the connectivity index as a multi-scaled, process-informed spatial aggregation of wildfire hazard properties across a landscape. We use a case study landscape to compare the connectivity index against simulated burn probability and historical burnt areas. Using a historically-informed parameterisation, we find a high correlation (0.83) to simulated burn probability with a fraction of the computational effort (0.3% of the runtime). The connectivity index also demonstrates an improved ability to explain historical burnt areas. We identify opportunities to further improve performance by incorporating the index into data-driven model structures.Our findings demonstrate that the connectivity index captures structural patterns in wildfire likelihood, as influenced by the interaction of fire behaviour factors across multiple scales. By achieving this in a computationally efficient manner, we believe that the connectivity index can work alongside other measures of wildfire likelihood to inform and plan wildfire risk reduction activities, including in large-scale analysis.

There has so far been no shared understanding of validity in agent-based simulation. We here conceptualise validation as systematically substantiating the premises on which conclusions from simulation analysis for a particular modelling context are built. Given such a systematic perspective, validity of agent-based models cannot be ensured if validation is merely understood as an isolated step in the modelling process. Rather, valid conclusions from simulation analysis require context-adequate method choices at all steps of the simulation analysis including model construction, model and parameter inference, uncertainty analysis and simulation. We present a twelve-step protocol to highlight the (often hidden) premises for methodological choices and their link to the modelling context. It is designed to aid modelers in understanding their context and in choosing and documenting context-adequate and mutually consistent methods throughout the modelling process. Its purpose is to assist reviewers and the community as a whole in assessing and discussing context-adequacy.