We review the current body of academic literature concerning gamification of production and logistics. The findings indicate that production execution and control has been addressed most often in the current body of literature, which consists mostly of design research. Objectives and goals, points, achievements, multimedial feedback, metaphorical/fictional representations, and levels and progress are currently most often employed gamification affordances on this field. The research has focused on examining or considering motivation, enjoyment and flow as the main psychological outcomes of gamification in the given context, while individual performance and efficiency are the most commonly examined or suggested behavioral/organizational impacts. Future studies should employ more rigorous study designs and firmly ground the discussions in organization theory.
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One of the goals of this research is to arrive at an implementation of a CAN-bus that can be used for lab exercises in regular student courses. In this paper, an overview is given of our basic ideas concerning the CAN concept and its application to the control of a manufacturing system. This system consists of two robots, a milling machine and some transportation means. In this system, every workstation will have its own CAN controller. The concept consists of a specially designed hardware structure, embedded software for the protocol and initialisation and a high level production environment, that makes it possible to configure a production system in an easy way.
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Next-generation sequencing technology allows culture- independent analysis of species and genes present in a complex microbial community. Such metagenomics may overcome the inability to culture microbes in isolation. Microbial communities of interest are for example responsible for making biogas. Many applications in metagenomics focus on 16S RNA analysis. We here evaluate the possibility of whole genome analysis (WGS) as approach for metagenomics studies.Samples (Table 1) from three biogas installations fed with different feedstock were used for DNA isolation and WGS analysis. Short (75b) Illumina paired-end DNA sequence reads were generated and assembled into larger continuous stretches (contigs),AcknowledgementsResults show that WGS is feasible for complex community analysis. Large groups of organisms (for example the class Methanomicrobia) are present in all samples with a possible role in the biogas production pathway.Assemble reads into contigs•meta-velveth as metagenomics reads assemblerSequencesimilaritysearch•proteome reference database from all currently available Bacteria and Achaea genomesAssign hits to taxa•Lowest common ancestor method incorporated in MEGAN4Such studies will help to identify and use microbial species for future improvements of biogas production dependence on process parameters and feedstock.
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Author supplied: The production system described in this paper in an im- plementation of an agile agent-based production system. This system is designed to meet the requirements of modern production, where short time to market, requirement-driven production and low cost small quan- tity production are important issues. The production is done on special devices called equiplets. A grid of these equiplets connected by a fast network is capable of producing a variety of diverent products in parallel. The multi-agent-based software infrastructure is responsible for the agile manufacturing. A product agent is responsible for the production of a single product and equiplet agents will perform the production steps to assemble the product. This paper describes this multiagent-based production system with the focus on the product agent.
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Er zijn veel verschillende sensoren beschikbaar die gebruikt kunnen worden om data in te winnen. Daarnaast zijn er veel verschillende werkwijzen om aan de slag te gaan met sensoren. Om een gestandaardiseerde werkwijze op te stellen, is een groep 4e-jaars AGIS studenten van de HAS green academy in het kader van het SURF project SMART sensordata infrastructuur aan de slag gegaan met het proces omtrent het inwinnen van data met sensoren. Hier is een werkwijze uit komen rollen die voor iedereen en overal werkt. In deze handleiding wordt de werkwijze stap voor stap uitgelegd.
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This paper explores how so-called ‘Web3’ blockchain projects are materially and socially constituted. A blockchain is an append-only distributed database. The technology is being hyped as applicable for a whole range of industries, social service provisions, and as a fix for economic disparities in communities left behind by mainstream financial systems. Drawing on case studies from our ongoing research we explain how, despite being virtual, Web3 projects are dependent on clearly defined spaces of production from which they derive their speculative value. We conceptualise this relationship as Crypto/Space, where space and blockchain software are mutually constituted. We consider how Crypto/Spaces are produced in three ways: 1) how project developers are adopting a parasitic relationship with host locations to appropriate energy, infrastructure, and local resources; 2) how projects enable ‘virtual land grabs’ where developers are engaging in land acquisitions, and associated displacement of local people, with no real intention to use the land for the declared purpose; and 3) how blockchain technology and speculative finance imaginaries are inspiring new anarcho-capitalist crypto-utopian ‘Exit zones’, often in the Global South. Far from being a zero-sum virtual game world, we argue that cryptocurrency projects are parasitic, often requiring predation on poor and otherwise marginalised communities to appropriate resources, onboard new users and enable favourable regulation.
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PURPOSE: The aim of this research is to link sustainability strategies with risk management. DESIGN/METHOD: 33 unique cases were used for the data analysis. Using the cases, the researchers built a database to operationalise the theoretical framework. This database contains data on general characteristics of an organisation, strategic characteristics (mission, vision, value proposition, core values from the Balanced Score Card categories, strategic goals), strategy characteristics of the sustainability strategies, the 17 sustainability goals of the UN, risks (strategic, financial, operational) and control measures appropriate to the risks. RESULTS/FINDINGS: The first sub-question: Which risks at a strategic, financial, and operational level differ in organisations that pursue SDG 3 Good health and wellbeing, SDG 8 Decent work and economic growth and/or SDG 12 Responsible consumption and production, or do not pursue sustainability goals? It can be answered that sustainable values lead to different risks at strategic and financial levels, but not on an operational level. The second sub-question: Which risks on a strategic, financial, and operational level differ in organisations that pursue the sustainability strategy (Retain product ownership, Product life extension and/or Design for recycling) or do not pursue a sustainability strategy? It can be answered in a similar way as the first research question: that apparently sustainable strategies lead to different risks at strategic and financial levels, but not on an operational level. Operational risks were found but did not change in case of the sustainable strategy. ORIGINALITY/VALUE: Researchers have investigated whether pursuing the sustainability strategy (part 1) or contributing to the achievement of SDGs (part 2) by an organisation causes a change in strategic, financial and/or operational risks. Patterns were sought, not the magnitude of a change, because of the number of cases examined.
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Met deze SchuldPreventiewijzer willen we iedereen die aan de slag wil met schuldpreventie op weg helpen. We hebben een strategisch stuk opgesteld over het belang van schuldpreventie. Ook hebben we een concreet stappenplan uitgewerkt om een bijdrage te leveren aan het ontwikkelen van lokaal preventiebeleid. Om elkaar op weg te helpen, hebben we een database ingericht met talloze voorbeelden van preventieactiviteiten. Door de activiteiten in de database te voorzien van een toelichting, achtergrondmateriaal en contactgegevens slaan we bruggen en bevorderen we de onderlinge uitwisseling. Door jullie uit te nodigen nieuwe activiteiten aan te leveren, beogen we van de database een centrum van kennisdeling op het terrein van schuldpreventie te maken. De database is toegankelijk via onze websites. Daarmee onderstrepen we onze samenwerking op het gebied van schuldpreventiebeleid.
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We tested the hypothesis that in human ageing a decreased intramuscular acylcarnitine status is associated with (pre-)frailty, reduced physical performance and altered mitochondrial function. Results showed that intramuscular total carnitine levels and acetylcarnitine levels were lower in (pre-)frail old females compared to fit old females and young females, whereas no differences were observed in males. The low intramuscular acetylcarnitine levels in females correlated with low physical performance, even after correction for muscle mass (%), and were accompanied with lowered expression of genes involved in mitochondrial energy production and functionality. We concluded that in (pre-)frail old females, intramuscular total carnitine levels and acetylcarnitine levels are decreased, and this decrease is associated with reduced physical performance and low expression of a wide range of genes critical for mitochondrial function. The results stress the importance of taking sex differences into account in ageing research.
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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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