Live programming is a style of development characterized by incremental change and immediate feedback. Instead of long edit-compile cycles, developers modify a running program by changing its source code, receiving immediate feedback as it instantly adapts in response. In this paper, we propose an approach to bridge the gap between running programs and textual domain-specific languages (DSLs). The first step of our approach consists of applying a novel model differencing algorithm, tmdiff, to the textual DSL code. By leveraging ordinary text differencing and origin tracking, tmdiff produces deltas defined in terms of the metamodel of a language. In the second step of our approach, the model deltas are applied at run time to update a running system, without having to restart it. Since the model deltas are derived from the static source code of the program, they are unaware of any run-time state maintained during model execution. We therefore propose a generic, dynamic patch architecture, rmpatch, which can be customized to cater for domain-specific state migration. We illustrate rmpatch in a case study of a live programming environment for a simple DSL implemented in Rascal for simultaneously defining and executing state machines.
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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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Research, advisory companies, consultants and system integrators all predict that a lot of money will be earned with decision management (business rules, algorithms and analytics). But how can you actually make money with decision management or in other words: Which business models are exactly available? In this article, we present seven business models for decision management.
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Objective: Self-management is a core theme within chronic care and several evidence-based interventions (EBIs) exist to promote self-management ability. However, these interventions cannot be adapted in a mere copy-paste manner. The current study describes and demonstrates a planned approach in adapting EBI’s in order to promote self-management in community-dwelling people with chronic conditions. Methods: We used Intervention Mapping (IM) to increase the intervention’s fit with a new context. IM helps researchers to take decisions about whether and what to adapt, while maintaining the working ingredients of existing EBI’s. Results: We present a case study in which we used IM to adapt EBI’s to the Flemish primary care context to promote self-management in people with one or more chronic disease. We present the reader with a contextual analysis, intervention aims, and content, sequence and scope of the resulting intervention. Conclusion: IM provides an excellent framework in providing detailed guidance on intervention adaption to a new context, while preserving the essential working ingredients of EBI’s. Practice Implications: The case study is exemplary for public health researchers and practitioners as a planned approach to seek and find EBI’s, and to make adaptations.
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In November 2019, scholars and practitioners from ten higher education institutions celebrated the launch of the iKudu project. This project, co-funded by Erasmus [1], focuses on capacity development for curriculum transformation through internationalisation and development of Collaborative Online International Learning (COIL) virtual exchange. Detailed plans for 2020 were discussed including a series of site visits and face-to-face training. However, the realities of the COVID-19 pandemic disrupted the plans in ways that could not have been foreseen and new ways of thinking and doing came to the fore. Writing from an insider perspective as project partners, in this paper we draw from appreciative inquiry, using a metaphor of a mosaic as our identity, to first provide the background on the iKudu project before sharing the impact of the pandemic on the project’s adapted approach. We then discuss how alongside the focus of iKudu in the delivery of an internationalised and transformed curriculum using COIL, we have, by our very approach as project partners, adopted the principles of COIL exchange. A positive impact of the pandemic was that COIL offered a consciousness raising activity, which we suggest could be used more broadly in order to help academics think about international research practice partnerships, and, as in our situation, how internationalised and decolonised curriculum practices might be approached. 1. KA2 Erasmus+ Cooperation for innovation and the exchange of good practices (capacity building in the field of Higher Education)
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This report describes the Utrecht regio with regard to sustainability and circular business models.
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The QuickScan CBM (Circular Business Model) offers an approach to develop a circular business model. It focuses primarily on the manufacturing industry, even though it can be used in other sectors. It consists of three parts: (1) an introduction with an explanation of backgrounds and central concepts, (2) knowledge maps of seven business models that together form a classification and (3) the actual QuickScan.An interactive application can be found at Business Model Lab. This last version is bilingual (Dutch and English). Regardless of the version, it can be used to develop a new CBM or adapt an existing business model based on a qualitative approach. The starting point is that better design and organisation of a CBM contributes to the transformation and transition towards a sustainable and circular economy.
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As people age, physiological changes affect their thermal perception, sensitivity and regulation. The ability to respond effectively to temperature fluctuations is compromised with physiological ageing, upsetting the homeostatic balance of health in some. As a result, older people can become vulnerable at extremes of thermal conditions in their environment. With population ageing worldwide, it is an imperative that there is a better understanding of older people’s thermal needs and preferences so that their comfort and wellbeing in their living environment can be optimised and healthy ageing achieved. However, the complex changes affecting the physiological layers of the individual during the ageing process, although largely inevitable, cannot be considered linear. They can happen in different stages, speeds and intensities throughout the ageing process, resulting in an older population with a great level of heterogeneity and risk. Therefore, predicting older people’s thermal requirements in an accurate way requires an in-depth investigation of their individual intrinsic differences. This paper discusses an exploratory study that collected data from 71 participants, aged 65 or above, from 57 households in South Australia, over a period of 9 months in 2019. The paper includes a preliminary evaluation of the effects of individual intrinsic characteristics such as sex, body composition, frailty and other factors, on thermal comfort. It is expected that understanding older people’s thermal comfort from the lens of these diversity-causing parameters could lead to the development of individualised thermal comfort models that fully capture the heterogeneity observed and respond directly to older people’s needs in an effective way. (article starts at page 13)
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New Dutch agrifood business models are emerging in response to economic, social and ecological pressures: new players arrive, new logistical pathways come to the fore and innovative consumer and farmer relationships – food coöperatives – are forged. How do new business models relate to reconfiguring the Dutch agrifood system? Our research combines future exploration (backcasting) and analysis of new business models. We developed three agrifood transition scenarios with various groups of stakeholders. For each scenario, we then analysed a specific, representative business model to explore the different roles of business models in agrifood transition. Business models in the “Added value in and with the countryside” already exist and occupy a niche in the market. However, a breakthrough of these business models require large-scale institutional and behavioural change. Business models in the “New products, specific markets” exist but are rare. They usually concern high-value specialist products that could result in widespread market change, but might require little institutional change. The “Sustainable production methods” most resembles the current system. Some associated business models become successful, but they have difficulty distinguishing themselves from conventional produce, which raises questions about whether business models are able to drive a transition in this direction. Thus, our results lend credence to the hypothesis that different transition pathways offer specific potential for and requirements of new business models.
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Paper presented at the International Sustainability Transitions conference 2018 (12-14 june) Manchester, UK. The Dutch agrifood regime is grinding to a halt. International economic pressures force Dutch farmers to further scale up and intensify their businesses, while food scandals and calamities as well as many and varied negative environmental impacts have led to an all-time low societal acceptance of the agrifood regime as well as a host of legislative measures to stifle further growth. Such a situation, in which regime pressures increasingly undermine the regime, represents a strong call for transition of the Dutch agrifood system.At the same time, new business models emerge: new players arrive, new logistical pathways come to the fore and innovative consumer and farmer relationships – food co-operatives – are forged. In a sense, the transition is already under way (cf. Hermans et al., 2010), with new business models forming an important backbone. However, the way forward is still a matter of great uncertainty and controversy: How do new business models relate to reconfiguring the Dutch agrifood system? We explore the hypothesis that different transition pathways put specific demands on the role of new business models. We studied various new business models in the Dutch agrifood system and their relations to three different transition pathways. Our research combines future exploration (backcasting) and analysis of new business models. In this research, we approach this question from two angles. First, we introduce a transition-oriented business model concept, in order to effectively link new business models to transition. Then we shortly touch upon the transition pathway typology introduced by Geels et al. (2016) and describe three different transition pathways for the Dutch agrifood system. We report on XX business models in each of these transition pathways. The paper ends with a discussion of the role of business models for different types of transition pathways.
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