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Mutual enhancement or one-way street

The integration of research activities in universities of applied sciences (UASs) has led to the transformation of these universities into organisations with two primary processes: research and education. Although many believe in the benefits of combining research and education in one organisation, which is referred to as synergy in this study, research–education synergies have rarely been empirically investigated, particularly in the UAS context. Thus, this research investigates the intended synergy between the research and education of UASs by conducting a document analysis of their university-wide strategic policy. The findings show that UASs aim for synergies among people, UAS organisations and outside UAS organisations, with a focus on education-oriented synergies. This study provides an initial understanding of the strategic aims of UASs considering research–education synergy. The findings provide direction and a framework for future research and form a base for making explicit strategic choices for research–education connections in universities.

Leadership moments: Understanding nurse clinician‐scientists' leadership as embedded sociohistorical practices

Nurse clinician-scientists are increasingly expected to show leadership aimed at transforming healthcare. However, research on nurse clinician-scientists' leadership (integrating researcher and practitioner roles) is scarce and hardly embedded in sociohistorical contexts. This study introduces leadership moments, that is, concrete events in practices that are perceived as acts of empowerment, in order to understand leadership in the daily work of newly appointed nurse clinician-scientists. Following the learning history method we gathered data using multiple (qualitative) methods to get close to their daily practices. A document analysis provided us with insight into the history of nursing science to illustrate how leadership moments in the everyday work of nurse clinician-scientists in the “here and now” can be related to the particular histories from which they emerged. A qualitative analysis led to three acts of empowerment: (1) becoming visible, (2) building networks, and (3) getting wired in. These acts are illustrated with three series of events in which nurse clinician-scientists' leadership becomes visible. This study contributes to a more socially embedded understanding of nursing leadership, enables us to get a grip on crucial leadership moments, and provides academic and practical starting points for strengthening nurse clinician-scientists' leadership practices. Transformations in healthcare call for transformed notions of leadership.

Eindrapportage DNA-Succesmeter

Sentiment polarity classification of corporate review data with a bidirectional Long-Short Term Memory (biLSTM) neural network architecture

A considerable amount of literature has been published on Corporate Reputation, Branding and Brand Image. These studies are extensive and focus particularly on questionnaires and statistical analysis. Although extensive research has been carried out, no single study was found which attempted to predict corporate reputation performance based on data collected from media sources. To perform this task, a biLSTM Neural Network extended with attention mechanism was utilized. The advantages of this architecture are that it obtains excellent performance for NLP tasks. The state-of-the-art designed model achieves highly competitive results, F1 scores around 72%, accuracy of 92% and loss around 20%.

MSP Challenge 2050 - Ecopath Link Design Document

Improving Book Search with AI

We present our ongoing work on upgrading the Amsterdam Public Library's book database search capabilities. So far, users have had to input the exact book title and/or author name without any typos or misspellings in order to retrieve any results. This is in sharp contrast with the manner in which users typically use the interface: they frequently search for books on a particular topic, input the names of the characters, or even ask fully-fledged questions. The aim of this project is therefore to enable smart search in natural language based on book content. The initial focus is on the Dutch language, with the possibility of including English and other languages later. In the first phase of the project, we built a proof-of-concept knowledge graph from a sample of the existing tabular database and enriched the data with named entities extracted from book summaries. Based on this first step, a user query like "Heeft u boeken over de Tweede Wereldoorlog in Amsterdam?" would yield all books that mention both WW2 and Amsterdam. We are currently working on augmenting the knowledge graph with embeddings, which will enable us to retrieve semantically similar results. The final step of the research involves integrating our knowledge graph with a pre-trained large language model.