Remanufacturing is a production practice that requires the work of producers, consumers, and the government. There are benefits associated with this production model, such as improving the environment, opportunities for cost savings, and others. However, it is essential to identify the factors that affect the possibility of acceptance of this production model. This research proposes a model based on different analysis methodologies and techniques of SEM (Structural Equations Modeling) and the method of PLS (Partial Least Squares). A total of 403 responses to the survey were collected from 1 November 2021 to 15 January 2022. For the data treatment, SPSS, Excel, and WarpPLS software were used to identify the variables, factors, and their direct and indirect effects among the latent variables, referring to a scheme focused on consumer perception based on the acquisition remanufactured products. This created model served as a reference to create and develop a design and repair strategy for White goods or similar products in handling, logistics, and repair. This design strategy was transformed into a business model based on a circular economy, particularly on a Product–Service System with social, economic, and environmental benefits for producers and consumers.
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This research focused on implement analysis to diagnose the viability to propose a design and repair strategy based on Product-Service System (PSS) and remanufacturing to preserve the value in white goods, more specifically laundry machines. The aim is to generate an alternative to the linear economy to redirect consumers to the circular economy, positively affecting the environment, the economy, and society, leading to responsible consumption. To achieve this, it is necessary to identify consumer behavior and the factors that intervene to buy remanufactured products. Also, find a timely methodology for the development of the PSS, analyze the ability to conserve added value, propose the strategy and verify its feasibility. The reach of this paper is establishing customer perception in the acceptance of remanufactured products in a circular economy model for white goods.
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The aim of this document is to outline the preliminary requirements and steps needed to fully establish frameworks for certification systems across Europe, specifically to support and incentivize the restoration of peatlands and to provide a framework for reducing GHG emissions from degraded and mismanaged peatlands on a large scale. This will ensure that peatlands across Europe fulfil their potential to become a net carbon sink by 2050, while optimizing ecosystem service provision in a way that is fully consistent with all the relevant European policies. This report covers the following topics: - Analysis of current Carbon Credit systems and other incentives to support wet peatlands. - Economic land use analysis relating to peatlands. - Outline of a framework to support rewetting and peatland restoration. - Recommendations for an Eco-Credit system across Europe.
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In discussions on smart grids, it is often stated that residential end-users will play a more active role in the management of the electric power system. Experience in practice on how to empower end-users for such a role is however limited. This paper presents a field study in the first phase of the PowerMatching City project in which twenty-two households were equipped with demand-response-enabled heating systems and white goods. Although end-users were satisfied with the degree of living comfort afforded by the smart energy system, the user interface did not provide sufficient control and energy feedback to support an active contribution to the balancing of supply and demand. The full potential of demand response was thus not realized. The second phase of the project builds on these findings by design, implementation and evaluation of an improved user interface in combination with two demand response propositions. © 2013 IEEE.
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This white paper is the result of a research project by Hogeschool Utrecht, Floryn, Researchable, and De Volksbank in the period November 2021-November 2022. The research project was a KIEM project1 granted by the Taskforce for Applied Research SIA. The goal of the research project was to identify the aspects that play a role in the implementation of the explainability of artificial intelligence (AI) systems in the Dutch financial sector. In this white paper, we present a checklist of the aspects that we derived from this research. The checklist contains checkpoints and related questions that need consideration to make explainability-related choices in different stages of the AI lifecycle. The goal of the checklist is to give designers and developers of AI systems a tool to ensure the AI system will give proper and meaningful explanations to each stakeholder.
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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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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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Urban nature enhancement is a theme that needs to be considered across different scales. From pocket parks and façade-greening to urban green infrastructure, biodiversity thrives best through connectivity.In the SIA-project, Nature-inclusive Area Development, four universities of applied sciences - Aeres University of Applied Sciences, Avans University of Applied Sciences, Amsterdam University of Applied Sciences, and Van Hall Larenstein University of AppliedSciences- researched three levels of area development to accelerate the transition to nature-inclusive area development. The study consisted of three case studies: Waarder Railway Zone (building), Knowledge Mile Park (KMP - street - Amsterdam), and AlmereCentre-Pampus (area).
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White paper Juridische dienstverleners, in het bijzonder advocaten kunnen zich op basis van hun expertise in de markt van het MKB onvoldoende onderscheiden. Concurrentiekracht zal vooral gezocht moeten worden in andere aspecten van de dienstverlening die er voor zorgen dat advocaten klantwaarde genereren. Klantwaarde als gecombineerde uitkomst van de perceptie van de klant van kosten en baten, leidt tot loyaal gedrag van klanten. In de voornamelijk aanbodgerichte advocatuur is dit een nieuw marketingprincipe. In dit artikel wordt derhalve beargumenteerd waarom advocatenkantoren de cliënt centraal moeten stellen in hun marketingdenken. Daarnaast pleiten de auteurs voor specifiek onderzoek naar factoren van klantwaarde en daaraan gerelateerde loyaliteit in de juridische dienstverlening, aangezien wetenschappelijke bevindingen aantonen dat deze factoren per businesscontext verschillen.
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We need mental and physical reference points. We need physical reference points such as signposts to show us which way to go, for example to the airport or the hospital, and we need reference points to show us where we are. Why? If you don’t know where you are, it’s quite a difficult job to find your way, thus landmarks and “lieux de memoire” play an important role in our lives.
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