For the first time in the Netherlands, the Adjustment Scales for Early Transition in Schooling (ASETS) have been applied to kindergarten and first-grade elementary school. A study was conducted to examine the relation between the different behavioral (phenotypes) and situational dimensions (situtypes) of the ASETS and learning performance on standardized language and numeracy tests. Results show that a proportion of children experience socioemotional and behavioral problems. Among boys, in particular, emotional or behavioral problems are significant. Furthermore, results show that these problems translate into a fairly consistent pattern of negative correlations with language and numeracy performance. These outcomes support the assertion that some children are not yet ready for school. It therefore seems important that the structured academic approach that is central to many methods used in early childhood education undergoes critical reflection, as by no means all target group children are ready for this approach.
Background: Sarcopenic obesity (SO) is an increasing phenomenon and has been linked to several negative health consequences. The aim of this umbrella review is the assessment of effectiveness and certainty of evidence of nutrition and exercise interventions in persons with SO. Method: We searched for meta-analyses of RCTs in PubMed, EMBASE and CENTRAL that had been conducted in the last five years, focusing on studies on the treatment and prevention of SO. The primary endpoints were parameters for SO, such as body fat in %, skeletal muscle mass index (SMMI), gait speed, leg strength and grip strength. The methodological quality was evaluated using AMSTAR and the certainty of evidence was assessed using GRADE. Results: Four systematic reviews with between 30 to 225 participants were included in the umbrella review. These examined four exercise interventions, two nutrition interventions and four interventions that combined nutrition and exercise. Resistance training was the most frequently studied intervention and was found to improve gait speed by 0.14 m/s to 0.17 m/s and lower leg strength by 9.97 kg. Resistance, aerobic, mixed exercise and hypocaloric diet combined with protein supplementation is not significantly effective on selected outcomes for persons with SO compared to no intervention. The low number of primary studies included in the reviews resulted in moderate to very low certainty of evidence. Conclusion: Despite the lack in certainty of evidence, resistance training may be a suitable intervention for persons with SO, in particular for improving muscle function. Nevertheless, further research is necessary to strengthen the evidence.
Studying real-time teacher-student interaction provides insight into student's learning processes. In this study, upper grade elementary teachers were supported to optimize their instructional skills required for co-constructing scientific understanding. First, we examined the effect of the Video Feedback Coaching intervention by focusing on changes in teacher-student interaction patterns. Second, we examined the underlying dynamics of those changes by illustrating an in-depth micro-level analysis of teacher-student interactions. The intervention condition showed significant changes in the way scientific understanding was co-constructed. Results provided insight into how classroom interaction can elicit optimal co-construction and how this process changes during an intervention.
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.
