Although Pedagogical Content Knowledge (PCK) is traditionally defined as a static quality that teachers possess and apply in practice, we conceive of PCK as constituted by a dynamic, mutually influencing process between teacher and students. This co-constructing process is expressed in real-time interaction and defined by us as Expressed Pedagogical Content Knowledge (EPCK). The aim of this study is to develop a practically usable instrument that can track the microgenetic moment-to-moment interaction that embodies EPCK, and that enables us to observe different features and levels of EPCK in the form of changes in this interaction dynamics. We were interested to know how EPCK emerges and develops on the short-term time scale of classroom interaction. After presenting a general account of complex dynamic systems based measurement of psychological constructs (e.g., PCK), we describe a coding scheme for teacher-student interactions, based on theoretical EPCK components. The instrument was applied in an empirical observation study of a visit to a mobile planetarium by a grade 3 primary school class. A principal factor analysis was used to find latent EPCK components. Results show, firstly, that the instrument was reliable. Secondly, the variables in the coding scheme were relevant in view of the underlying theory. Thirdly, over the time course of the teaching session, latent components displayed various levels of EPCK – high, low or no ECPK. Instead of being an enduring or stable property of teaching-learning interactions, EPCK is a dynamic property occurring in the form of sequences of high and low levels, and corresponding peaks in the latent factors. Notably, EPCK did not appear in the form of a continuous steady state level but occurred in the form of bursts of high-level EPCK. We conclude that our coding scheme provides an adequate method for studying pedagogical content knowledge as it self-organizes in the form of real-time activity in the classroom.
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We present the Brigade renderer: an efficient system that uses the path tracing algorithm to produce images for real-time games. We describe the architecture of the Brigade renderer, and provide implementation details. We describe two games that have been created using Brigade.
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New technologies or approaches are being widely developed and proposed to be deployed in real energy systems to improve desired objectives; however, supporting decision making processes to select best solutions in terms of performance and efficiently following cost-benefit analysis require some sort of scientific evidence based tools. These tools should be reliable, robust, and capable of demonstrating the behaviour and impact of newly developed devices or algorithms in different pre- defined scenarios. Therefore, new approaches and technologies need to be tested and verified using a safe laboratory test environment.This report is about the development and realisation of some major tools and reliable methods to calculate risks and opportunities for integrating of new energy resources into the European electricity grid. Hanze University Groningen and Politecnico di Torino worked together within the STORE&GO project sharing laboratories, knowledge, hardware facilities and researchers for the realisation of the characterisation and mathematical modelling of renewable resources. Needed to realize a stable and reliable environment for remote physical hardware in the loop simulations.For this realisation we started with the local characterisation of a PV-Field and a PEM electrolyser at Entrance Groningen by logging and measuring the electric behaviour and specific device parameters to integrate and convert these into working mathematical models of a PV-Field and electrolyser prosumer. After testing and evaluating these models by comparing the results with the real-time measurements, these test and modelling is also realised from the remote laboratory in Torino. To achieve dynamical physical hardware we also realised dynamic mathematical model(s) with real-time functionality to interact directly with the remote electrolyser. To connect both the laboratories with full duplex communication functionalities between physical hardware and models we have also realized a network which is able to share network resources on both local and remote sites.
The utilization of drones in various industries, such as agriculture, infrastructure inspection, and surveillance, has significantly increased in recent years. However, navigating low-altitude environments poses a challenge due to potential collisions with “unseen” obstacles like power lines and poles, leading to safety concerns and equipment damage. Traditional obstacle avoidance systems often struggle with detecting thin and transparent obstacles, making them ill-suited for scenarios involving power lines, which are essential yet difficult to perceive visually. Together with partners that are active in logistics and safety and security domains, this project proposal aims at conducting feasibility study on advanced obstacle detection and avoidance system for low-flying drones. To that end, the main research question is, “How can AI-enabled, robust and module invisible obstacle avoidance technology can be developed for low-flying drones? During this feasibility study, cutting-edge sensor technologies, such as LiDAR, radar, camera and advanced machine learning algorithms will be investigated to what extent they can be used be to accurately detect “Not easily seen” obstacles in real-time. The successful conclusion of this project will lead to a bigger project that aims to contribute to the advancement of drone safety and operational capabilities in low-altitude environments, opening new possibilities for applications in industries where low-flying drones and obstacle avoidance are critical.
Real-Time Cyber-Physical Systems (RT-CPS) zijn onmisbaar in onze samenleving, van medische apparatuur tot autonome voertuigen. De betrouwbaarheid en robuustheid van deze systemen zijn echter cruciaal, fouten kunnen immers grote gevolgen hebben. Dit project beoogt de betrouwbaarheid van RT-CPS te vergroten door middel van een modulaire hardware-architectuur en geavanceerde validatie- en verificatiemethoden (V&V). In samenwerking met praktijkpartners, waaronder het Wilhelmina Kinderziekenhuis, wordt een proof-of-concept demonstrator ontwikkeld in een praktijkgerichte casus. De modulaire hardware-architectuur maakt RT-CPS flexibeler, toekomstbestendig en breed toepasbaar. De geavanceerde V&V-methoden borgen de betrouwbaarheid van de systemen en helpen MKB-bedrijven bij de ontwikkeling van hun eigen RT-CPS-applicaties. Naast de directe voordelen voor de betrokken partners, draagt dit project bij aan een bredere maatschappelijke impact. De verhoogde betrouwbaarheid van RT-CPS kan leiden tot verbeterde veiligheid en efficiëntie in diverse sectoren. Een krachtige samenwerking tussen kennisinstituten, praktijkpartners en het MKB is de sleutel tot succes. Dit project bundelt expertise en praktijkkennis om Nederland een leidende positie te laten innemen op het gebied van betrouwbare RT-CPS. In dit 1-jarig verkennend project zal de Hogeschool van Arnhem en Nijmegen samenwerken met Gemini Embedded Technology, Wilhelmina Kinderziekenhuis, het grootbedrijf Capgemini en de Universiteit Utrecht.
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.
