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An online tool for Schematic Mapping in Geography education
Get PDFSketch maps, or croquis, are a tool widely applied in research and education. In geography secondary education in particular, sketch maps are helping the pupils to geographically structure information, and to distinguish between main and side issues. Various researchers (Schee 2017, 2020, Plomp 2013) have concluded that this works best if the students create their own sketch maps, because it trains their geographic thinking and helps them connect the knowledge and skills they acquired to the curriculum. In this research, a web-based sketch map making tool was created, based on requirements from didacticians, teachers and pupils active in secondary school geography education in The Netherlands. The requirements were acquired from literature research, surveys and an extensive focus group session. A prototype tool was created and tested in four sessions in different classroom settings. The tool was aimed at providing not only basic drawing functionality for creating maps, but also to provide flexibility to change and correct mistakes made easily. Also, the tool provides the possibility for teachers to create assignments, for different levels of pupils, each with their specific tool settings.In this paper we present the design process of the tool and discuss its potential for geography education.<br/
The impact of spatiotemporal variability of environmental conditions on wheat yield forecasting using remote sensing data and machine learning
Get PDFClimate change poses significant challenges to food security, especially in semi-arid agriculture areas. Effective monitoring of crop yield is important for establishing food emergency responses and developing long-term sustainable strategies. In Morocco, where cereals are the predominant crops, yield forecasting is important for addressing the yield gap as it enables farmers to take preventive actions before the harvesting period. This study aims to assess the impact of spatial and temporal heterogeneity of environmental conditions on wheat yield forecasting using machine learning models. It compares the 2019–2020 and 2020–2021 agricultural seasons using three sets of variables: (1) spectral indices; (2) weather data; and (3) a combination of both spectral indices and weather data. Weather data, including cumulative monthly precipitation from ERA5 data and average monthly temperature from PERSIANN data, were extracted for the wheat growing season (November to June). Spectral indices including the Normalized Difference Vegetation Index, Moisture Stress Index, and Terrestrial Chlorophyll Index were calculated from Sentinel-2 imagery for the same period and processed using Google Earth Engine. The study area was divided into homogeneous zones based on an existing landform classification, and XGBoost and Random Forest (RF) models were used for yield forecasting in each zone separately. The two models performed equally well across both the zones and the whole study area (SA) when using weather data as the input variable. For instance, across SA, they achieved average R 2 values of 0.60 and 0.81 for all months during the 2019–2020 and 2020–2021 agricultural seasons, respectively. However, when using spectral indices or combining these indices with weather data, RF consistently outperformed XGBoost. For example, in SA during the 2019–2020 season, RF achieved an average R 2 of 0.48 across the growing season, compared to XGBoost's R 2 of 0.43. Similarly, in the 2020–2021 season, RF achieved an R 2 of 0.35 and an RMSE of 1083 kg ha -1, while XGBoost performed slightly lower, with an R 2 of 0.29 and an RMSE of 1137 kg ha -1. Comparing the prediction accuracy between the seasons for each set of variables, the RF model performs better when using spectral indices during the relatively dry 2019–2020 season as compared to the wet 2020–2021 season. Incorporating weather data, the model improved its performance for the 2020–2021 season. April showed the highest prediction performance overall, with R 2 values of 0.6 for SA using weather data alone in the 2019–2020 season, and 0.8 for SA using a combination of weather data and spectral indices in the 2020–2021 season. The 2019–2020 season showed strong fluctuations in accuracy throughout the growing season, whereas the 2020–2021 season had a consistent improvement in accuracy over time. These variations in accuracy are due to differing environmental conditions that should be taken into account for making better and more reliable yield predictions.</p
Towards automatic delineation of landslide source and runout
Get PDFMapping landslide-depleted source areas is pivotal for refining predictive models and volume estimations, yet these critical regions are often conflated with landslide runouts, leading to sub-optimal assessments. The source (or scarp) areas are typically the regions where the actual failure occurs, providing crucial information on the initiation mechanisms and the nature of landslide propagation. Catering to this objective, we built a method based on a landslide's topology and morphological information to delineate the source and runout margins. We develop and test this method in geomorphologically distinct regions such as Dominica, Turkey, Italy, Nepal, and Japan (Niigata) to showcase the model's robust adaptive capacity. The model can demarcate the source and runout zones from landslide planforms found in inventories with accuracy deviations under 15%–20%. While distinguishing landslide source and runout areas, the model also considers triggering information and movement types. We also deploy the model in Chile, Japan (Hokkaido), Colombia, Papua New Guinea, and China. In these new regions, we found the mean area of the scarp to be consistently under 30% of the total landslide area. We additionally showcased the application of our model to the area–volume scaling of the coseismic landslides triggered by the 2018 Hokkaido Eastern Iburi Earthquake (MW 6.6) in Japan. Our analysis revealed that area–volume fitting using the landslide source areas instead of the total landslide planforms or polygons improves the linear fit from R2=0.49 to R2=0.81. Our work could improve diverse landslide analysis, such as hazard and runout models, and facilitate a deeper understanding of landslide behaviour.</p
Strengthening all-of-society approaches for disaster resilient societies through competency building:A European research agenda
No full textThe increasing frequency of disasters, alongside the recent COVID-19 pandemic, climate emergency, and ongoing/new crises including conflicts and their disproportionate impacts on many communities, all point towards the cascading, multidimensional, and systemic nature of risks. In the wake of this ever-changing risk landscape, it is paramount to adopt multi-sectoral, multidisciplinary risk reduction, preparedness, and adaptation approaches, which are inclusive and innovative, and which reduce vulnerability. In line with the recent midterm review of the implementation of the Sendai Framework for Disaster Risk Reduction, this calls for nuanced and critical actions at all levels based on strategies to increase risk awareness and vulnerability reduction, which are co-developed and enabled through all-of-society engagement and participation. This paper builds from the research and experience of more than 8 European-funded research projects involving over 100 research and practitioner organisations, which has shown the positive impacts of all-of-society approaches for involving members of the population in areas of disaster risk management (DRM), disaster risk reduction (DRR), and climate change adaptation (CCA). The paper presents evidence-based insights and lessons learnt from these European projects focusing on improving engagement between authorities and citizens and building capacities through inclusive participatory actions. This includes reflections on diverse methodological approaches leading to integrated outcomes. Based on the outcomes of the projects, we propose four key-dimensions of investing in disaster resilient societies: 1) enhancing the participation of multiple stakeholders, and 2) building capacities in order to 3) reduce vulnerabilities, enabled by 4) organizational change leading to the adaptability of formal DRM organisations’ routines and operating structures. Key outcomes and recommendations from the projects are provided to guide future research, policy, and practice on all-of-society engagement for strengthening societal resilience to disasters with a specific focus on competency building among populations at-risk.</p
An insight into parameter identifiability issues in the Carreau–Yasuda model:A more consistent rheological formulation for shear-thinning non-Newtonian inelastic fluids
Get PDFThe Carreau–Yasuda rheological model is widely employed in both research and industrial applications to describe the shear-thinning behaviour of non-Newtonian inelastic fluids. However, the model parameter traditionally employed to characterize the shear thinning response exhibits only a weak correlation with the actual shear thinning rate observed in experimental data. This limitation leads to intrinsic identifiability issues, which may result in misleading physical interpretations of the model parameters and unreliable flow predictions. Aiming to contribute to overcoming these issues, this paper introduces a novel heuristic rheological formulation for shear-thinning non-Newtonian inelastic fluids, as an alternative to the Carreau–Yasuda model. Analytical results and exemplary numerical case studies demonstrate that the proposed formulation is based on physically meaningful model parameters, whose identifiability is not compromised by the key limitations of the Carreau–Yasuda model. The new approach allows for effective parameter estimation through a straightforward direct identification strategy, eliminating the need for inverse identification procedures based on nonlinear regression techniques. Moreover, the proposed formulation naturally enables the identication of two Carreau numbers based on the two characteristic shear rates of the fluid.</p
Introducing a framework for designing an interdisciplinary engineering curriculum:educating new engineers for complex sociotechnical challenges
Get PDFTackling complex challenges emerging from sociotechnical systems requires close collaboration among different disciplinary experts to develop adequate and well-informed solutions. This requirement has profound implications for how education for future engineers is designed and delivered. The curriculum of a Dutch liberal arts programme that concentrates on interdisciplinary engineering education (IEE) was re-evaluated and redesigned to strengthen the integration of knowledge, skills, and attitudes in a project-based education. This curriculum redesign process resulted in a framework that specifies the focal points (proficiency in disciplinary knowledge, research and design skills, communication and collaboration abilities, and academic mindset) of the educational programme’s intended learning outcomes and emphasises the interconnectedness among those focal points throughout the education of interdisciplinary engineering students. This emphasis on interconnection is predicated on the integration of the central propositions of theories (e.g., constructivism, system theory) and frameworks (e.g. conceive-design-implement-operate or CDIO, project-based learning) in interdisciplinary engineering education.</p
Genetic Engineering of VHH Antibody Fragments for Efficient Site-Specific Conjugation to Polysaccharides
Get PDFSite-selective modifications of proteins, without compromising their biological activity, are highly sought after due to their critical role in many biomedical applications. Here, we established a universal and efficient approach for site-selective conjugation of a variable domain of single-chain heavy-chain only antibody fragments (VHH) to polysaccharides using thiol-maleimide chemistry, known for its specificity and efficiency. This is achieved by genetically engineering an unpaired cysteine (Cys) residue in a C-terminal extension of VHHs. In this study, we synthesized two maleimide-functionalized polysaccharides, i.e., dextran-maleimide (Dex-Mal) and hyaluronic acid-maleimide (HA-Mal), for protein conjugation. Six distinct VHHs were selected and engineered with C-terminal extensions containing Cys residues for conjugation with Dex-Mal and HA-Mal. Conjugation efficiency varied among VHHs due to structural heterogeneity, which influenced the reactivity of the engineered Cys residues. One VHH, specific to TNFα (anti-TNFα-VHH), exhibited low conjugation efficiency (<20%); however, efficiency was fully restored when a flexible glycine-serine G4S linker was introduced between the variable domain and the C-terminal Cys tag. Additionally, incorporation of two free Cys residues in the C-terminal tail further enhanced conjugation efficiency. This work establishes a robust and versatile approach for generating protein-polysaccharide conjugates, paving the way for therapeutic and diagnostic applications.</p
A Slant-Polarized Coaxial Slot Array Antenna Based on Gap Waveguide MLW Technology for E-Band Applications
No full textThis paper presents the design of a coaxial slot array antenna based on gap waveguide multilayer waveguide (MLW) technology for E-Band applications. The proposed array consists of two subarrays in a 2 × 4 configuration, with each element designed to generate 45° slant polarization. The design features a five-layer structure, which enables slant polarization by progressively rotating the electric field orientation by 45° through intermediate cavities. The antenna achieves an impedance bandwidth of S11 < -10 dB from 75.8 - 81.8 GHz. With 8 radiating slots, the maximum achieved gain is 14.7dBi.</p
Multimodal rationales for explainable visual question answering
No full textVisual Question Answering (VQA) is a challenging task of predicting the answer to a question about the content of an image. Prior works directly evaluate the answering models by simply calculating the accuracy of predicted answers. However, the inner reasoning behind the predictions is disregarded in such a "black box" system, and we cannot ascertain the trustworthiness of the predictions. Even more concerning, in some cases, these models predict correct answers despite focusing on irrelevant visual regions or textual tokens. To develop an explainable and trustworthy answering system, we propose a novel model termed MRVQA (Multimodal Rationales for VQA), which provides visual and textual rationales to support its predicted answers. To measure the quality of generated rationales, a new metric vtS (visual-textual Similarity) score is introduced from both visual and textual perspectives. Considering the extra annotations distinct from standard VQA, MRVQA is trained and evaluated using samples synthesized from some existing datasets. Extensive experiments across three EVQA datasets demonstrate that MRVQA achieves new state-of-the-art results through additional rationale generation, enhancing the trustworthiness of the explainable VQA model. The code and the synthesized dataset are released under https://github.com/lik1996/MRVQA2025