Using the past to orientate on the present and the future can be seen as one of history’s main contributions to educating future citizens of democratic societies. Because teachers often lack useful methods for pursuing this goal, this study explores three pedagogical approaches that may help them making connections between the past, the present and the future: working with longitudinal lines (LL), with enduring human issues (EHI) and with historical analogies (HA). The efficacy of these approaches was examined in three case studies conducted in two Dutch secondary schools with eighth- to tenth-grade students (N=135) and their teachers (N=4) as participants. Explorations took place within the boundaries of the existing history curriculum and in close collaboration with the teachers who participated because they felt a need to motivate their students by means of a pedagogy to make history more useful. Findings suggest that implementing the LL- and EHI-approaches in a traditional history curriculum with chronologically ordered topics is more complicated than implementing the HA-approach. The HA-approach appears to have more potential to encourage students to use historical knowledge in present-day contexts than the other two approaches. In terms of students’ appraisals of the relevance of history, the application of the EHI-approach showed positive effects.
Learning mathematical thinking and reasoning is a main goal in mathematical education. Instructional tasks have an important role in fostering this learning. We introduce a learning sequence to approach the topic of integrals in secondary education to support students mathematical reasoning while participating in collaborative dialogue about the integral-as-accumulation-function. This is based on the notion of accumulation in general and the notion of accumulative distance function in particular. Through a case-study methodology we investigate how this approach elicits 11th grade students’ mathematical thinking and reasoning. The results show that the integral-as-accumulation-function has potential, since the notions of accumulation and accumulative function can provide a strong intuition for mathematical reasoning and engage students in mathematical dialogue. Implications of these results for task design and further research are discussed.
In this pilot study, we investigated the feasibility of a home-based, remotely guided exercise intervention for patients with gliomas. The six-month intervention included three home-based exercise sessions per week at 60%–85% of maximum heart rate. Participants wore heart rate monitors connected to an online platform to record activities that were monitored weekly by the physiotherapist.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
A transition to a circular economy is needed to revolutionize the construction sector and make it more sustainable for present and future generations. While the construction industry and the production of construction materials contribute to environmental pollution, they also offer great potential for addressing many environmental problems. Sheet materials are engineered wood boards that are produced from recycled or solid wood where an adhesive is used to bind the particles together, predominantly used in: Furniture manufacturing, Flooring application, Roofing, Wall sheathing. The most common binder for boards is urea-formaldehyde. Other binders may be used depending on the grade of board and its intended end-use. For example, melamine urea-formaldehyde, phenolic resins and polymeric diphenylmethane diisocyanate (PMDI) are generally used in boards that require improved moisture resistance. Formaldehyde is classified in the in the European Union as a carcinogen and it carries the hazard statement 'suspected of causing cancer'. In this project mycelium composites are developed as a formaldehyde-free, fully natural and biodegradable material with high potential to substitute these hazardous materials. The heat-press process, the feasibility of which was evaluated in a previous Kiem HBO project, is to be further developed towards a process where mycelium sheets with different thicknesses will be obtained. This is considered as a fundamental step to increase the material approachability to the market. Different Material manufacturing techniques are also considered to enable the increase of sample thicknesses and volume. Moreover, a business study will be incorporated to allow further understanding of the material market potential. The consortium composition of V8 Architects, QbiQ, Fairm, Verbruggen Paddestoelen BV, and CoEBBE merges different expertise and guarantees the consideration of the whole material production chain. The research will contribute to bring mycelium composites a step closer to the market, giving them visibility and increasing the possibility to a commercial breakthrough.
