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Teaching and Learning Chemical Bonding in a Cutting-edge Research Context
Get PDFTo increase the relevance and motivation for learning chemistry in secondary schools, the Dutch national curriculum adopts a context-based approach. This means that learning is situated in real(istic) contexts which provide compelling motivation for learners to gain practical experience alongside subject-matter understanding. When properly designed, materials can support the enactment of context-based learning. However, source materials that do so adequately remain somewhat scarce. Further, while previous research shows that cutting-edge research has the potential to serve as an interesting context for pre-university students, very few learning materials situated in this type of context are available. This study set out to articulate relevant pedagogical content knowledge for one set of context-based materials (on the topic of chemical bonding), to define essential criteria for learning materials situated in cutting-edge research contexts, and to provide insight into how teachers make sense of teaching and learning within one such context using materials alone.To achieve these goals, a design-based study was undertaken in which student materials and a corresponding teacher guide were developed. First, a systematic literature review on crucial pedagogical content knowledge for teaching chemical bonding was undertaken. This resulted in a framework for teaching the topic, which is valuable in its own right and was also used to design materials which foster student learning about chemical bonding while situated in the context of cutting-edge research on early cancer diagnosis. Then, through three cycles of iterative testing and refinement, investigations were carried out, focusing on student learning about chemical bonding and their motivation for learning chemistry in this setting. Additionally, the study sought to ascertain if and how teachers developed pedagogical content knowledge when working with the designed materials. In general, both students and teachers positively experienced working with the materials. Student understanding of chemical bonding was comparable to regular practice, but they were more motivated when working with the context-based learning materials. Further, the findings demonstrate that teachers experienced the learning materials as usable and educative. Some growth in pedagogical content knowledge was observed. This study gives insight into how context-based teaching and learning can be supported in a sustainable, scalable way, i.e. through the provision of materials only.<br/
Exploring a Large Language Model for Transforming Taxonomic Data into OWL:Lessons Learned and Implications for Ontology Development
Get PDFManaging scientific names in ontologies that represent species taxonomies is challenging due to the ever-evolving nature of these taxonomies. Manually maintaining these names becomes increasingly difficult when dealing with thousands of scientific names. To address this issue, this paper investigates the use of ChatGPT-4 to automate the development of the Organism module in the Agricultural Product Types Ontology (APTO) for species classification. Our methodology involved leveraging ChatGPT-4 to extract data from the GBIF Backbone API and generate OWL files for further integration in APTO. Two alternative approaches were explored: (1) issuing a series of prompts for ChatGPT-4 to execute tasks via the BrowserOP plugin and (2) directing ChatGPT-4 to design a Python algorithm to perform analogous tasks. Both approaches rely on a prompting method where we provide instructions, context, input data, and an output indicator. The first approach showed scalability limitations, while the second approach used the Python algorithm to overcome these challenges, but it struggled with typographical errors in data handling. This study highlights the potential of Large language models like ChatGPT-4 to streamline the management of species names in ontologies. Despite certain limitations, these tools offer promising advancements in automating taxonomy-related tasks and improving the efficiency of ontology development
Isothermal and non-isothermal crystallization kinetics modelling of neat and composite PA410
No full textThe increased interest in semi-crystalline thermoplastic composites, calls for a profound understanding of the intricate relationship between process parameters and the development of crystalline structures. In this study, the non-isothermal multi-phase crystallization kinetics of PA410 is modelled with the Schneider rate equations. Moreover, the change in crystallization kinetics induced by the addition of carbon-black nanoparticles and glass-fibres to PA410, is quantified by means of Flash-DSC experiments. To this end, a new sample preparation procedure to make unidirectional fibre-reinforced samples, of known fibre volume fraction, suitable for Flash-DSC analysis is introduced and used for the characterization of the reinforced PA410. The investigations showed that the polymorphism of neat PA410 is altered by the introduction of carbon black, which suppresses the formation of β-phase crystals, leading to an α-phase dominant crystalline volume. This finding simplified the crystallization kinetics modelling of both CB-filled and GF-reinforced PA410, for which one single set of Schneider rate equations was sufficient to accurately describe their crystallization behaviour, under isothermal and non-isothermal conditions
Ultrasound-actuated microfluidic flow focusing allows control of bubble size and production rate
Get PDFMicrofluidic flow focusing is a promising tool for the creation of monodisperse microbubbles, such as ultrasound contrast agents. However, industrial-scale bubble production remains elusive due to a lack of control over bubble size and production rate in devices with paralellized flow-focusing nozzles. In this paper we introduce a way of fine-tuning the production rate and bubble size through the use of ultrasound excitation. We show that the production rate can be locked to the ultrasound driving frequency and that the bubble size depends on the acoustic driving pressure. These results may be exploited in particular to increase monodispersity in parallelized systems.</p
What have urban digital twins contributed to urban planning and decision making?:A systematic literature review and a socio-technical research and development agenda
Get PDFUrban Digital Twins (UDTs) were first discussed in 2018. Seven years later we ask: What has been their contribution to urban planning and decision-making so far? Here, we systematically review 84 peer-reviewed articles to map and compare UDTs’ ambitions with their realized contributions. Our results indicate that despite the vast technical developments, socio-technical challenges have remained largely unaddressed which caused much of the UDTs’ ambitions to remain unrealized. We identify three categories in these socio-technical challenges: interdisciplinary integration (II), consensual contextualization (CC), and procedural operationalization (PO). Accordingly, we consolidate a socio-technical research and development agenda to realize the ambitions of UDTs for urban planning and decision-making: Augmented Urban Planning (AUP)
A collaborative and scalable geospatial data set for arctic retrogressive thaw slumps with data standards
Get PDFArctic permafrost is undergoing rapid changes due to climate warming in high latitudes. Retrogressive thaw slumps (RTS) are one of the most abrupt and impactful thermal-denudation events that change Arctic landscapes and accelerate carbon feedbacks. Their spatial distribution remains poorly characterised due to time-intensive conventional mapping methods. While numerous RTS studies have published standalone digitisation datasets, the lack of a centralised, unified database has limited their utilisation, affecting the scale of RTS studies and the generalisation ability of deep learning models. To address this, we established the Arctic Retrogressive Thaw Slumps (ARTS) dataset containing 23,529 RTS-present and 20,434 RTS-absent digitisations from 20 standalone datasets. We also proposed a Data Curation Framework as a working standard for RTS digitisations. This dataset is designed to be comprehensive, accessible, contributable, and adaptable for various RTS-related studies. This dataset and its accompanying curation framework establish a foundation for enhanced collaboration in RTS research, facilitating standardised data sharing and comprehensive analyses across the Arctic permafrost research community.</p
Do vegetated intertidal areas alter currents on an estuarine scale?
Get PDFIntertidal areas often host tidal marsh vegetation, which plays a crucial role in influencing hydrodynamic processes. This study investigates the influence of saltmarsh vegetation on fringing and mid-channel flats on currents in the Western Scheldt, the Netherlands. We quantify how and over which spatial scale vegetated intertidal areas affect estuarine currents, and how this varies over time when vegetation cover changes. Using vegetation maps spanning ca 25 years, we assessed changes in saltmarsh vegetation extent and distribution, with notable vegetation expansion on mid-channel flats. A depth-averaged Delft3D-FM model was adopted to quantify the effect of both mono-specific and multi-species vegetation on peak ebb and flood currents. Results indicate that species diversity is particularly important when considering currents at the marsh-mudflat scale, and its effect is negligible at the broader estuarine scale. During storm conditions, mono-specific and multi-species vegetation reduced peak velocities by 10–80% on the vegetated zone, and by 1–30% in the surroundings, over an area comparable to or smaller than the vegetated zone. During calm-weather conditions, the effect was limited to the vegetated zone (10–80%, although with smaller magnitude and extent compared to storm conditions). Over the period 1993–2016, both bathymetrical changes and changes in vegetation had a comparable magnitude of impact on altering current velocities over the saltmarshes. These findings emphasize that saltmarsh vegetation alters currents at the marsh-mudflat scale in particular. This will have estuary-wide implications for sediment dynamics, morphology and ecology over a timescale of decades
Design Principles and Multifunctionality of Flood Resilient Landscapes
Get PDFWith escalating climate risks, the design of flood-resilient landscapes that prioritize multifunctionality has become essential for sustainable urban and rural planning. Hybrid flood risk management (FRM) solutions, integrating nature-based solutions (NbS) such as floodplain restoration, wetland restoration, and riparian buffers with technical measures like levees and retention basins, present a multifunctional approach to enhancing flood resilience specifically in riverine systems. Recent studies emphasize the need for strategies that not only mitigate flood risks but also offer co-benefits such as biodiversity enhancement, carbon sequestration, and recreational opportunities. Additionally, as prolonged periods of low flows and droughts become increasingly common due to climate change, it is crucial to assess NbS effectiveness under these conditions. Studies such as van Brink et al. (2022) underscore the necessity of understanding the resilience of NbS during both high-flow and low-flow periods, ensuring they contribute to long-term water availability, habitat stability, and ecosystem health (van Brenk et al., 2022).When it comes to flood-resilient landscapes, more evidence is needed to mainstream nature-based solutions alone or their combination with traditional engineering measures as hybrid flood risk management strategies (Awah et al., 2024). This research investigates key design principles that enhance flood resilience while delivering added ecological, social, and economic co-benefits. Through a detailed analysis of recent literature, expert interviews, and collaborative workshops, this study highlights the strategies that enable landscapes to function as both flood mitigation assets and multifunctional community resources.The review examines the literature to (1) provide an overview of the current state of research on the impact of hybrid approaches for flood resilience, (2) group the different design principles/frameworks for assessing the performance of hybrid approaches by identifying the main research areas, methods, types of measures and their achieved benefits, and (3) highlight important future prospects in hybrid measures for flood resilience and their multifunctionality. Additionally, river case studies—representing diverse hydraulic and morphological conditions—are used to showcase the best and worst practices. The findings will demonstrate how hybrid FRM solutions provide enhanced flood mitigation while supporting other benefits such as habitat connectivity, water quality improvement, and local economic development
Significant expansion of small water bodies in the Dongting Lake region following the impoundment of the Three Gorges Dam
Get PDFSmall water bodies (SWBs) are vital for freshwater biodiversity and ecosystem services, yet they remain underrepresented in research compared to larger water bodies, despite being particularly vulnerable to anthropogenic activities and climate change. Advances in satellite remote sensing, particularly the Joint Research Centre’s Global Surface Water (JRC-GSW) dataset derived from Landsat imagery, provide an unprecedented opportunity for high-resolution and long-term analysis of SWBs spatio-temporal dynamics. This study leverages the JRC-GSW dataset to assess the impacts of the Three Gorges Dam (TGD) on SWBs in the Dongting Lake region, focusing on maximum and minimum water extents during wet and dry seasons pre- and post-dam impoundment. Results reveal significant shrinkage and fragmentation of water bodies post-TGD, particularly during the dry season and predominantly in the northwest region. The number and total area of SWBs increased post-TGD by 16%–83% and 17%–28%, respectively, accompanied by intensified seasonal variability. Enhanced fragmentation was especially pronounced during the dry season. Weak correlations between water body dynamics and hydrometeorological factors highlight the dominant influence of anthropogenic activities, particularly dam operations, in shaping these patterns. These findings emphasize the importance of high-resolution, long-term satellite data in monitoring SWBs dynamics and inform sustainable water resource management and biodiversity conservation in regions affected by large-scale infrastructure projects
Application of transfer learning on physics-based models to enhance vessel shaft power predictions
Get PDFInternational shipping must reduce its emissions to meet global targets, with improving energy efficiency being a crucial step in this journey. Predictive models are essential for implementing energy efficiency measures such as weather routing, scheduling of hull cleanings, and just-in-time arrival, which are vital for reducing fuel consumption and emissions.This paper explores the use of transfer learning to integrate physics-based and data-driven models for predicting vessel shaft power under varying operating conditions. Physics-based models rely on principles of resistance and propulsion, whereas data-driven models employ advanced machine learning techniques utilizing high-frequency operational data. A novel approach is proposed that integrates synthetic data from physics-based simulations with real operational data via transfer learning. This method enhances model accuracy while significantly reducing the amount of data required, and therefore the time until sufficient data is collected to develop a reliable data-driven model.The proposed method is demonstrated on an ocean-going vessel use case to predict the shaft power demand in varying conditions. The results reveal that the proposed transfer learning approach outperforms regular data-driven methods, both in accuracy and required training time. The approach thus offers a robust solution for predicting vessel performance, demonstrating improved model accuracy and reduced dependency on extensive real-world data for training