This paper highlights the use of State Space Grids (SSGs) for studying real-time classroom discourse in an intervention targeting professional development. State Space Grid analysis is both a powerful way to visualise patterns in interactional data, and a starting point for further quantitative analysis. In the present study SSGs were used to explore patterns in teacher–student interactions. The study shows the importance of using micro-level time-serial data and illustrates how change in interactions during and after an intervention can be studied. SSG analysis was applied to study interaction in terms of the coupling of a teacher and a student variable: autonomy support and musical creativity. Video data from 40 music lessons of five teachers and their classes was used as input for plotting teacher–student interactions in SSGs, consisting of two dimensions. SSGs allow visualising change in the situation of interactions in the grid and identifying change in patterns to different grid areas. The findings show how interactions tended to settle in areas representing more productive interaction for all but one class. We discuss the benefits of using SSGs in intervention studies and the implications for educational practice and research of using this time-serial approach.
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Author supplied: Abstract—The growing importance and impact of new technologies are changing many industries. This effect is especially noticeable in the manufacturing industry. This paper explores a practical implementation of a hybrid architecture for the newest generation of manufacturing systems. The papers starts with a proposition that envisions reconfigurable systems that work together autonomously to create Manufacturing as a Service (MaaS). It introduces a number of problems in this area and shows the requirements for an architecture that can be the main research platform to solve a number of these problems, including the need for safe and flexible system behaviour and the ability to reconfigure with limited interference to other systems within the manufacturing environment. The paper highlights the infrastructure and architecture itself that can support the requirements to solve the mentioned problems in the future. A concept system named Grid Manufacturing is then introduced that shows both the hardware and software systems to handle the challenges. The paper then moves towards the design of the architecture and introduces all systems involved, including the specific hardware platforms that will be controlled by the software platform called REXOS (Reconfigurable EQuipletS Operating System). The design choices are provided that show why it has become a hybrid platform that uses Java Agent Development Framework (JADE) and Robot Operating System (ROS). Finally, to validate REXOS, the performance is measured and discussed, which shows that REXOS can be used as a practical basis for more specific research for robust autonomous reconfigurable systems and application in industry 4.0. This paper shows practical examples of how to successfully combine several technologies that are meant to lead to a faster adoption and a better business case for autonomous and reconfigurable systems in industry.
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The ‘Grand Challenges’ of our times, like climate change, resource depletion, global inequity, and the destruction of wildlife and biodiversity can only be addressed by innovating cities. Despite the options of tele-working, tele-trading and tele-amusing, that allow people to participate in ever more activities, wherever they are, people are resettling in cities at an unprecedented speed. The forecasted ‘rurification’ of society did not occur. Technological development has drained rural society from its main source of income, agriculture, as only a marginal fraction of the labour force is employed in agriculture in the rich parts of the world. Moreover, technological innovation created new jobs in the IT and service sectors in cities. Cities are potentially far more resource efficient than rural areas. In a city transport distances are shorter, infrastructures can be applied to provide for essential services in a more efficient way and symbiosis might be developed between various infrastructures. However, in practice, urban infrastructures are not more efficient than rural infrastructures. This paper explores the reasons why. It digs into the reasons why the symbiotic options that are available in cities are not (sufficiently) utilised. The main reason for this is not of an economic nature: Infrastructure organisations are run by experts who are part of a strong paradigmatic community. Dependence on other organisations is regarded as limiting the infrastructure organisation’s freedom of action to achieve its own goals. Expert cultures are transferred in education, professional associations, and institutional arrangements. By 3 concrete examples of urban systems, the paper will analyse how various paradigms of experts co-evolved with evolving systems. The paper reflects on recent studies that identified professional education as the initiation into such expert paradigms. It will thereby relate lack of urban innovation to the monodisciplinary education of experts and the strong institutionalised character of expertise. https://doi.org/10.1007/978-3-319-63007-6_43 LinkedIn: https://www.linkedin.com/in/karelmulder/
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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Residential public charging points are shared by multiple electric vehicle drivers, often neighbours. Therefore, charging behaviour is embedded in a social context. Behaviours that affect, or are influenced by, other publiccharging point users have been sparsely studied and lack an overarching and comprehensive definition. Consequently, very few measures are applied in practice to influence charging behaviour. We aim to classify and define the social dimension of charging behaviour from a social-psychological perspective and, using a behaviour change framework, identify and analyse the measures to influence this behaviour. We interviewed 15 experts onresidential public charging infrastructure in the Netherlands. We identified 17 charging behaviours rooted in interpersonal interactions between individuals and interactions between individuals and technology. These behaviours can be categorised into prosocial and antisocial charging behaviours. Prosocial charging behaviour provides or enhances the opportunity for other users to charge their vehicle at the public charging point, for instance by charging only when necessary. Antisocial charging behaviour prevents or diminishes this opportunity, for instance by occupying the charging point after charging, intentionally or unintentionally. We thenidentified 23 measures to influence antisocial and prosocial charging behaviours. These measures can influence behaviour through human–technology interaction, such as providing charging etiquettes to new electric vehicle drivers or charging idle fees, and interpersonal interaction, such as social pressure from other charging point users or facilitating social interactions to exchange requests. Our approach advocates for more attention to the social dimension of charging behaviour.
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Abstract. In recent years circular economy has become more important for the development of many places including cities. Traditionally, urban development policies have mainly been aiming to improve the socio-economic wellbeing of neighbourhoods. However, technical and ecologic aspects have their effects too and need to go hand in hand. This paper is based on an urban area experiment in the Dutch city of Utrecht. In order to assess urban area developments, typically rather straight-forward quantitative indicators have been used. However, it has proved more complicated to assess multifaceted developments of the area studied in this paper. With the City Model Canvas a multi-layered model is being used to better assess the impact of the urban development being studied. Key findings include that the project studied resulted in more space for companies from the creative industry and the settlement of local ‘circular’ entrepreneurs and start-ups, although it remains unclear to what extent these benefit from each other’s presence. The increase in business activity resulted in more jobs, but it is again unclear whether this led to more social inclusion. From an environmental point of view the project activities resulted in less raw materials being used, although activities and public events bring nuisance to the surrounding neighbourhoods.
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Acknowledging the knowledge gaps and novel paradigms outlined above within both coaching research and practice, the PhD research aims to investigate how need-supportive coaching—rooted in Self-Determination Theory (SDT)—effectively fulfils the satisfaction of beginning teachers’ basic psychological needs (BPNs) and autonomous motivation in pursuing their coaching goals within dyadic coaching relationships. To systematically explore this overarching objective, this PhD project and thesis adopt a structured, four-step approach, where each step investigates specific and complementary aspects of the coaching process. Collectively, these steps provide a comprehensive examination of how and under what conditions BPN-supportive coaching facilitates optimal motivational outcomes, enriching our understanding of the dynamic processes that shape coaching effectiveness among beginning teachers. Specifically, four research questions systematically guide the four research steps:1. What is the current state of empirical evidence linking BPN support, BPN satisfaction, and autonomous motivation within coach-coachee relationships?2. How do perceptual distances between coaches’ and coachees’ perceptions regarding BPN support relate to the coachees’ BPN satisfaction?3. How do coaches’ and coachees’ BPN satisfaction mutually predict autonomous motivation toward shared goals in their dyadic relationships?4. How do coaches and coachees dynamically co-regulate BPN interactions in real-time dyadic coaching conversations?Chapter 1 outlined a four-step empirical approach across Chapters 2 to 5 to understand BPN-supportive coaching. Chapter 2 meta-analysed the extant literature guided by a circular framework connecting BPN support, BPN satisfaction, and autonomous motivation for both parties. The results revealed that previous studies predominantly used individual-level data, neglecting the dynamic, reciprocal interactions in coaching. Chapter 3 investigated perceptual distance between coaches and coachees regarding BPN support using polynomial regression and response surface analysis. Results indicated that coachees reported greater BPN satisfaction when perceptions were closely aligned. Chapter 4 adopted the Actor-Partner Interdependence Model to examine how both parties’ BPN satisfaction is associated with their own and each other’s autonomous motivation. We found a positive unidirectional association between coachees’ relatedness satisfaction and coaches’ autonomous motivation and bidirectional associations for autonomy satisfaction. Chapter 5 employed State Space Grid analyses to capture the moment-to-moment co-regulation of autonomy in coach-coachee dyads. Results revealed recurrent patterns of predominant functional co-regulation (e.g., autonomy support met with proactive autonomy expression), and occasional dysfunctional co-regulation (e.g., evaluative feedback met with disengagement). Temporal evolvement in autonomy co-regulation was identified across coaching sessions in response to changing goals. Chapter 6 synthesised the contributions of the thesis. Collectively, BPN-supportive coaching can be viewed as a context-sensitive, interdependent, co-regulatory, and dynamic process, and we provided guidance for adaptive and relationally grounded coaching practices.
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Recent developments in digital technology and consumer culture have created new opportunities for retail and brand event concepts which create value by offering more than solely marketing or transactions, but rather a place where passion is shared. This chapter will define the concept of ‘fashion space’ and consumer experience, and delves into strategies for creating experiences that both align with a brand’s ethos and identity and build brand communities. It will provide insight on creating strong shared brand experiences that integrate physical and digital spaces, AR and VR. These insights can be used for consumer spaces but also for media and buyer events, runway shows, test labs and showrooms. Since its launch in 2007, international fashion brand COS has focused on creating fashion spaces that build and reinforce a COS fashion community. COS retail stores with their extraordinary architecture, both traditional and contemporary, contribute stories and facilitate intense brand experiences. Moreover, COS’ dedication to share the artistic inspirations of its people led to collaborating on interactive and multi-sensory installations which allow consumers to affectively connect to the brand’s personality and values. Thus, the brand was able to establish itself firmly in the lifestyle of its customers, facilitating and developing their aesthetics and values. This is an Accepted Manuscript of a book chapter published by Routledge/CRC Press in "Communicating Fashion Brands. Theoretical and Practical Perspectives" on 03-03-2020, available online: https://www.routledge.com/Communicating-Fashion-Brands-Theoretical-and-Practical-Perspectives/Huggard-Cope/p/book/9781138613560. LinkedIn: https://nl.linkedin.com/in/overdiek12345
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Many visitor studies make social background variables the central point of departure to explain participation patterns. How the past is 'staged', however, also has an influence on those to whom it appeals. This relational perspective calls for new conceptual tools to grasp empirical reality. Inspired by the historical philosophy of Georg Simmel and the literary theory of Mikhail Bakhtin a number of concepts which enable us to grasp the subtle relationship between museum presentations and visitors are presented. Bakhtin's notion of chronotopy serves as a key concept. By linking museum presentations and visitor perceptions with each other, it is also possible to identify certain tendencies within the contemporary museum landscape.
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Author supplied: "This paper gives a linearised adjustment model for the affine, similarity and congruence transformations in 3D that is easily extendable with other parameters to describe deformations. The model considers all coordinates stochastic. Full positive semi-definite covariance matrices and correlation between epochs can be handled. The determination of transformation parameters between two or more coordinate sets, determined by geodetic monitoring measurements, can be handled as a least squares adjustment problem. It can be solved without linearisation of the functional model, if it concerns an affine, similarity or congruence transformation in one-, two- or three-dimensional space. If the functional model describes more than such a transformation, it is hardly ever possible to find a direct solution for the transformation parameters. Linearisation of the functional model and applying least squares formulas is then an appropriate mode of working. The adjustment model is given as a model of observation equations with constraints on the parameters. The starting point is the affine transformation, whose parameters are constrained to get the parameters of the similarity or congruence transformation. In this way the use of Euler angles is avoided. Because the model is linearised, iteration is necessary to get the final solution. In each iteration step approximate coordinates are necessary that fulfil the constraints. For the affine transformation it is easy to get approximate coordinates. For the similarity and congruence transformation the approximate coordinates have to comply to constraints. To achieve this, use is made of the singular value decomposition of the rotation matrix. To show the effectiveness of the proposed adjustment model total station measurements in two epochs of monitored buildings are analysed. Coordinate sets with full, rank deficient covariance matrices are determined from the measurements and adjusted with the proposed model. Testing the adjustment for deformations results in detection of the simulated deformations."
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