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Happy Running?

This paper explores a method for deducing the affective state of runners using his/her movements. The movements are measured on the arm using a smartphone’s built-in accelerometer. Multiple features are derived from the measured data. We studied which features are most predictive for the affective state by looking at the correlations between the features and the reported affect. We found that changes in runners’ movement can be used to predict change in affective state.

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AI-based exhaust gas temperature

Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.

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Prima vera: Synergising predictive maintenance

The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.

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PrimaVera

The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions https://doi.org/10.3390/app10238348 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/

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Building and building systems energy prediction models to enhance energy flexibility control

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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Capturing Urban Memory with Sentient Data, Counter Mapping and Digital Methods

This paper puts forward a conceptual proposition that ties the discourses on ‘urban memory’ (Stillman and Johanson, 2009; Ringas, Christopoulou, Stefanidakis., 2011; Loughran, Fine & Hunter, 2015), sensory ethnography (Pink 2017 ), and counter-mapping (Crampton and Krygier 2018; ) with digital methods (Rogers, Sánchez-Querubín, and Kil, 2015). As an ‘interventionist’ approach, we understand co-producing counter (dynamic) maps with local stakeholders (actors), coupled with sensory and sentient data as a way of capturing the memory of urban peripheral landscapes (through intervention and participation) and thus creating archival knowledge.Urban memory is often understood as a form of collective memory that isconstituted by individual experiences within the place itself and through its historyand social environment (Ringas et al., 2011). With rapid changes in digitaltechnologies, digital and material have become “inseparate and entangled inenvironments people move and navigate their lives through'' (Pink and Fors, 2017).Memories are “evoked with material engagement with devices” which “opens up afield of sensory and affective engagement” research (ibid). While Pink and Forspropose to follow such engagement in a mundane and everyday setting, seen as anon-representational, phenomenological approach, we put forward a mixedmethods approach that connects sensory and sentient data (as agents) with the largerenvironmental context.Urban areas are often conceptualized as sites of ‘creative destruction’, in between stability and change, space (that can be developed) and place (that is lived in), often subjected to planning, regulation, and economic forces (Batty, 2007). This is especially true for urban areas that are located outside of the ‘center’ or in the cities’ periphery. These areas have experienced an endless cycle of deconstruction and reconstruction often witnessed and captured by local inhabitants, creatives, and activists. Currently, many of the peripheral areas are emancipating, bringing forward and openly communicating their complexities, values, and engaging various stakeholders in their regeneration efforts (which happens in a broader context of many European cities repositioning themselves in more polycentric and polyphonic ways, (Scott, 2015).To be able to capture the memory of ever-changing, ‘built a new’ urban places, we put forward counter (dynamic) mapping using digital methods as complemented with sensory and sentient data generated through interactions with digital technologies. Building on Crampton’s notion of maps (Crampton and Krygier, 2018), cartography is understood as existence (becoming) rather than essence (fixed ontology). Maps are therefore taken not as ‘objects’, but as performative practices. Digital methods, on the other hand, enable us to understand dynamic place-making, through ‘tracing’ the stakeholders (actors) and their relations overtime to capture the ways the urban environment gets performed.To clarify with an example, in Spinoza Imaginaries Lab & Cafe situated inAmsterdam Southeast we have been capturing the ever changing urbanenvironment in partnership with local stakeholders (actors), mapping their evolvingrelationships (and grouping) using the IssueCrawler and sentient data co-gatheredby researchers and students, with the clear understanding that to be able to capturea place, it is important to map the vernacular knowledge of that place (imaginaries,including art, movies, unrealized plans and initiatives, etc.). We propose this mixedmethods approach as an epistemological practice geared towards archiving thedynamic state of urban peripheral landscapes.

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Capturing Urban Memory with Sentient Data, Counter Mapping and Digital Methods

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In-hospital risk prediction for post-stroke depression

The timely detection of post-stroke depression is complicated by a decreasing length of hospital stay. Therefore, the Post-stroke Depression Prediction Scale was developed and validated. The Post-stroke Depression Prediction Scale is a clinical prediction model for the early identification of stroke patients at increased risk for post-stroke depression. he study included 410 consecutive stroke patients who were able to communicate adequately. Predictors were collected within the first week after stroke. Between 6 to 8 weeks after stroke, major depressive disorder was diagnosed using the Composite International Diagnostic Interview. Multivariable logistic regression models were fitted. A bootstrap-backward selection process resulted in a reduced model. Performance of the model was expressed by discrimination, calibration, and accuracy.

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International ring trial of the epidermal equivalent sensitizer potency assay

This article gives information on an international ring trial of the epidermal-equivalent (EE) sensitizer potency assay.

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International ring trial of the epidermal equivalent sensitizer potency assay

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Prediction of carcinogenic potential of chemicals using repeated-dose (13-week) toxicity data

From the article: Abstract Sub-chronic toxicity studies of 163 non-genotoxic chemicals were evaluated in order to predict the tumour outcome of 24-month rat carcinogenicity studies obtained from the EFSA and ToxRef databases. Hundred eleven of the 148 chemicals that did not induce putative preneoplastic lesions in the sub-chronic study also did not induce tumours in the carcinogenicity study (True Negatives). Cellular hypertrophy appeared to be an unreliable predictor of carcinogenicity. The negative predictivity, the measure of the compounds evaluated that did not show any putative preneoplastic lesion in de sub-chronic studies and were negative in the carcinogenicity studies, was 75%, whereas the sensitivity, a measure of the sub-chronic study to predict a positive carcinogenicity outcome was only 5%. The specificity, the accuracy of the sub-chronic study to correctly identify non-carcinogens was 90%. When the chemicals which induced tumours generally considered not relevant for humans (33 out of 37 False Negatives) are classified as True Negatives, the negative predictivity amounts to 97%. Overall, the results of this retrospective study support the concept that chemicals showing no histopathological risk factors for neoplasia in a sub-chronic study in rats may be considered non-carcinogenic and do not require further testing in a carcinogenicity study.

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Happy Running?

AI-based exhaust gas temperature

Prima vera: Synergising predictive maintenance

PrimaVera

Building and building systems energy prediction models to enhance energy flexibility control

Capturing Urban Memory with Sentient Data, Counter Mapping and Digital Methods

In-hospital risk prediction for post-stroke depression

International ring trial of the epidermal equivalent sensitizer potency assay

Prediction of carcinogenic potential of chemicals using repeated-dose (13-week) toxicity data

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Happy Running?

AI-based exhaust gas temperature

Prima vera: Synergising predictive maintenance

PrimaVera

Building and building systems energy prediction models to enhance energy flexibility control

Capturing Urban Memory with Sentient Data, Counter Mapping and Digital Methods

In-hospital risk prediction for post-stroke depression

International ring trial of the epidermal equivalent sensitizer potency assay

Prediction of carcinogenic potential of chemicals using repeated-dose (13-week) toxicity data