Data is widely recognized as a potent catalyst for advancing healthcare effectiveness, increasing worker satisfaction, and mitigating healthcare costs. The ongoing digital transformation within the healthcare sector promises to usher in a new era of flexible patient care, seamless inter-provider communication, and data-informed healthcare practices through the application of data science. However, more often than not data lacks interoperability across different healthcare institutions and are not readily available for analysis. This inability to share data leads to a higher administrative burden for healthcare providers and introduces risks when data is missing or when delays occur. Moreover, medical researchers face similar challenges in accessing medical data due to thedifficulty of extracting data from applications, a lack of standardization, and the required data transformations before it can be used for analysis. To address these complexities, a paradigm shift towards a data-centric application landscape is essential, where data serves as the bedrock of the healthcare infrastructure and is application agnostic. In short, a modern way to think about data in general is to go from an application driven landscape to a data driven landscape, which will allow for better interoperability and innovative healthcare solutions.In the current project the research group Digital Transformation at Hanze University of Applied Sciences works together with industry partners to build an openEHR implementation for a Groningen-based mental healthcare provider.
Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and metereological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be GatedRecurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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In dit artikel willen wij bezien in hoeverre het belang van verantwoording en content voor de performance van bedrijfsprocessen in instrumenten voor organisatieverandering een rol speelt. In de praktijk blijken er in vele organisaties problemen te bestaan bij het afleggen van verantwoording en het waarborgen van contentkwaliteit (Bussel, Ector 2009). En dat terwijl in het grootste deel van de bestaande organisaties voortdurend geprobeerd wordt de performance van de bedrijfsprocessen te verbeteren door het inzetten van verschillende instrumenten van organisatieverandering.
The reclaiming of street spaces for pedestrians during the COVID-19 pandemic, such as on Witte de Withstraat in Rotterdam, appears to have multiple benefits: It allows people to escape the potentially infected indoor air, limits accessibility for cars and reduces emissions. Before ordering their coffee or food, people may want to check one of the many wind and weather apps, such as windy.com: These apps display the air quality at any given time, including, for example, the amount of nitrogen dioxide (NO2), a gas responsible for an increasing number of health issues, particularly respiratory and cardiovascular diseases. Ships and heavy industry in the nearby Port of Rotterdam, Europe’s largest seaport, exacerbate air pollution in the region. Not surprisingly, in 2020 Rotterdam was ranked as one of the unhealthiest cities in the Netherlands, according to research on the health of cities conducted by Arcadis. Reducing air pollution is a key target for the Port Authority and the City of Rotterdam. Missing, however, is widespread awareness among citizens about how air pollution links to socio-spatial development, and thus to the future of the port city cluster of Rotterdam. To encourage awareness and counter the problem of "out of sight - out of mind," filmmaker Entrop&DeZwartFIlms together with ONSTV/NostalgieNet, and Rotterdam Veldakademie, are collaborating with historians of the built environment and computer science and public health from TU Delft and Erasmus University working on a spatial data platform to visualize air pollution dynamics and socio-economic datasets in the Rotterdam region. Following discussion of findings with key stakeholders, we will make a pilot TV-documentary. The documentary, discussed first with Rotterdam citizens, will set the stage for more documentaries on European and international cities, focusing on the health effects—positive and negative—of living and working near ports in the past, present, and future.
The automobile industry is presently going through a rapid transformation towards autonomous driving. Nearly all vehicle manufacturers (such as Mercedes Benz, Tesla, BMW) have commercial products, promising some level of vehicle automation. Even though the safe and reliable introduction of technology depends on the quality standards and certification process, but the focus is primarily on the introduction of (uncertified) technology and not on developing knowledge for certification. Both industry and governments see the lack of knowledge about certification, which can ensure the safety of autonomous technology and thus will guarantee the safety of the driver, passenger, and environment. HAN-AR recognized the lack of knowledge and the need for novel certification methodology for emerging vehicle technology and initiated the PRAUTOCOL project together with its SME partners. The PRAUTOCOL project investigated certification methodology for two use-cases: certification for automated highway overtaking pilot; and certification for automatic valet parking. The PRAUTOCOL research is conducted in two parallel streams: certification of the driver by human factors experts and certification of vehicle by technology experts. The results from both streams are published and presented in respective but limited target groups. Also, an overview of the PRAUTOCOL certification methodology is missing, which can enable its translation to different use-cases of automated technology (other than the used ones). Therefore, to realize a better pass-through of PRAUTOCOL's results to a broader audience, the top-up is required. Firstly, to write a (peer-reviewed) Open Access article, focusing on the application and translation of PRAUTOCOL's methodology to other automated technology use-cases. Secondly, to write a journal article, focusing on the validation of automatic highway overtaking system using naturalistic driving data. Thirdly, to organize a workshop to present PRAUTOCOL's results (valorization) to industrial, research, and government representatives and to discuss a follow-up initiative.
In summer 2020, part of a quay wall in Amsterdam collapsed, and in 2010, construction for a parking lot in Amsterdam was hindered by old sewage lines. New sustainable electric systems are being built on top of the foundations of old windmills, in places where industry thrived in the 19th century. All these examples have one point in common: They involve largely unknown and invisible historic underground structures in a densely built historic city. We argue that truly circular building practices in old cities require smart interfaces that allow the circular use of data from the past when planning the future. The continuous use and reuse of the same plots of land stands in stark contrast with the discontinuity and dispersed nature of project-oriented information. Construction and data technology improves, but information about the past is incomplete. We have to break through the lack of historic continuity of data to make building practices truly circular. Future-oriented construction in Amsterdam requires historic knowledge and continuous documentation of interventions and findings over time. A web portal will bring together a range of diverse public and private, professional and citizen stakeholders, each with their own interests and needs. Two creative industry stakeholders, Yume interactive (Yume) and publisher NAI010, come together to work with a major engineering office (Witteveen+Bos), the AMS Institute, the office of Engineering of the Municipality of Amsterdam, UNESCO NL and two faculties of Delft University of Technology (Architecture and Computer Science) to inventorize historic datasets on the Amsterdam underground. The team will connect all the relevant stakeholders to develop a pilot methodology and a web portal connecting historic data sets for use in contemporary and future design. A book publication will document the process and outcomes, highlighting the need for circular practices that tie past, present and future.
