Many students in secondary schools consider the sciences difficult and unattractive. This applies to physics in particular, a subject in which students attempt to learn and understand numerous theoretical concepts, often without much success. A case in point is the understanding of the concepts current, voltage and resistance in simple electric circuits. In response to these problems, reform initiatives in education strive for a change of the classroom culture, putting emphasis on more authentic contexts and student activities containing elements of inquiry. The challenge then becomes choosing and combining these elements in such a manner that they foster an understanding of theoretical concepts. In this article we reflect on data collected and analyzed from a series of 12 grade 9 physics lessons on simple electric circuits. Drawing from a theoretical framework based on individual (conceptual change based) and socio-cultural views on learning, instruction was designed addressing known conceptual problems and attempting to create a physics (research) culture in the classroom. As the success of the lessons was limited, the focus of the study became to understand which inherent characteristics of inquiry based instruction complicate the process of constructing conceptual understanding. From the analysis of the data collected during the enactment of the lessons three tensions emerged: the tension between open inquiry and student guidance, the tension between students developing their own ideas and getting to know accepted scientific theories, and the tension between fostering scientific interest as part of a scientific research culture and the task oriented school culture. An outlook will be given on the implications for science lessons.
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The application of DC grids is gaining more attention in office applications. Especially since powering an office desk would not require a high power connection to the main AC grid but could be made sustainable using solar power and battery storage. This would result in fewer converters and further advanced grid utilization. In this paper, a sustainable desk power application is described that can be used for powering typical office appliances such as computers, lighting, and telephones. The desk will be powered by a solar panel and has a battery for energy storage. The applied DC grid includes droop control for power management and can either operate stand-alone or connected to other DC-desks to create a meshed-grid system. A dynamic DC nano-grid is made using multiple self-developed half-bridge circuit boards controlled by microcontrollers. This grid is monitored and controlled using a lightweight network protocol, allowing for online integration. Droop control is used to create dynamic power management, allowing automated control for power consumption and production. Digital control is used to regulate the power flow, and drive other applications, including batteries and solar panels. The practical demonstrative setup is a small-sized desktop with applications built into it, such as a lamp, wireless charging pad, and laptop charge point for devices up to 45W. User control is added in the form of an interactive remote wireless touch panel and power consumption is monitored and stored in the cloud. The paper includes a description of technical implementation as well as power consumption measurements.
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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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Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx BlockRAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution (1080 × 1920) @103 fcps.
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Public lighting’s primary purpose is nighttime visibility for security and safety. How to meet so many requirements of so many stakeholders? The key to developing a good plan is to relate lighting to functions of public spaces, because street lighting is more than a technical requirement, a security need, or a design element. It can be thought of and utilized in terms of how the type, placement, and wattage affect how a street is perceived and used. With present-day used street lighting systems however, flexibility is expensive, as is maintenance and energy consumption. A new solution is to use LED lighting with a Direct Current power system. Advantages are a decrease in: energy conversions; material use; amount of switch- boxes; components; labour costs and environmental comfort. The overall implementation of LED and DC will result in better control and efficient maintenance due to integrated bidirectional communication. A challenge is the relatively high investment for these new solutions. Another challenge; DC is not a standard yet in rules and regulations. In the paper the transition to direct current public lighting system will be described with all the pros and cons. A new concept of public ownership, to overcome financial challenges will be discussed. M Hulsebosch1, P Willigenburg2 ,J Woudstra2 and B Groenewald3 1CityTec b.v., Alblasserdam, The Netherlands 2The Hague University of Applied Sciences, The Hague, The Netherlands 3Cape Peninsula University of Technology, Cape Town, South Africa 10.1109/ICUE.2014.6904186
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Discussions about the importance of the built environment for healthcare delivery extend at least as far back as Hippocrates 1 (400 BC). The iconic Florence Nightingale (1859) also strongly believed in the influence the indoor environment has on the progress of disease and recovery. Today, the role of the built environment in the healing process is of growing interest to healthcare providers, environmental psychologists, consultants, and architects. Although there is a mounting evidence 1 linking healthcare environments to health outcomes, because of the varying quality of that evidence, there has also been a lack of clarity around what can and cannot be achieved through design. Given the ageing of society and the ever increasing numbers of persons with dementia in the Western World, the need for detailed knowledge about aged care environments has also become increasingly important. The mental and physical health state of these persons is extremely fragile and their needs demand careful consideration. Although environmental interventions constitute only a fraction of what is needed for people with dementia to remain as independent as possible, there is now sufficient evidence (2, 3) to argue they can be used as a first-line treatment, rather than beginning with farmalogical interventions.
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With the rise of the knowledge-based economy, Higher Education Institutions not only have to produce (under)graduates that are skilled in their profession but who also are competent as knowledge workers. This study focused on the enabling competences of the knowledge worker. Our aim was to develop a framework of enabling competences of knowledge work and to devise instruments to help undergraduate students create awareness of their own areas of competence in relation to knowledge work. We developed a Personal Priority Questionnaire (PPQ) that can be used in a one hour facilitated group discussion, and a Personal Mastery Questionnaire (PMQ) that can be used for individual awareness raising. We did a preliminary test of the PPQ within the competence-based educational program of a university of professional education in The Netherlands and found that facilitated group discussion seems to be an effective method of raising awareness in relation to the competences of the knowledge worker
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tract Micro wind turbines can be structurally integrated on top of the solid base of noise barriers near highways. A number of performance factors were assessed with holistic experiments in wind tunnel and in the field. The wind turbines underperformed when exposed in yawed flow conditions. The theoretical cosθ theories for yaw misalignment did not always predict power correctly. Inverter losses turned out to be crucial especially in standby mode. Combination of standby losses with yawed flow losses and low wind speed regime may even result in a net power consuming turbine. The micro wind turbine control system for maintaining optimal power production underperformed in the field when comparing tip speed ratios and performance coefficients with the values recorded in the wind tunnel. The turbine was idling between 20%–30% of time as it was assessed for sites with annual average wind speeds of three to five meters per second without any power production. Finally, the field test analysis showed that inadequate yaw response could potentially lead to 18% of the losses, the inverter related losses to 8%, and control related losses to 33%. The totalized loss led to a 48% efficiency drop when compared with the ideal power production measured before the inverter. Micro wind turbine’s performance has room for optimization for application in turbulent wind conditions on top of noise barriers. https://doi.org/10.3390/en14051288
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Whitepaper: The use of AI is on the rise in the financial sector. Utilizing machine learning algorithms to make decisions and predictions based on the available data can be highly valuable. AI offers benefits to both financial service providers and its customers by improving service and reducing costs. Examples of AI use cases in the financial sector are: identity verification in client onboarding, transaction data analysis, fraud detection in claims management, anti-money laundering monitoring, price differentiation in car insurance, automated analysis of legal documents, and the processing of loan applications.
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