This method paper presents a template solution for text mining of scientific literature using the R tm package. Literature to be analyzed can be collected manually or automatically using the code provided with this paper. Once the literature is collected, the three steps for conducting text mining can be performed as outlined below:• loading and cleaning of text from articles,• processing, statistical analysis, and clustering, and• presentation of results using generalized and tailor-made visualizations.The text mining steps can be applied to a single, multiple, or time series groups of documents.References are provided to three published peer reviewed articles that use the presented text mining methodology. The main advantages of our method are: (1) Its suitability for both research and educational purposes, (2) Compliance with the Findable Accessible Interoperable and Reproducible (FAIR) principles, and (3) code and example data are made available on GitHub under the open-source Apache V2 license.
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Drawing on a multiple case study of acquisitions of UK biopharmaceutical firms, we develop an analytical framework that elucidates how key determinants of the knowledge base of science-based firms and their combinations through M&As interact and affect post-acquisition investment in the target's R&D projects. We show that two factors - the complementarity/similarity of the technology, and the complementarity/similarity of the discovery and development capabilities of the target and acquiring firm - interact to produce different outcomes in terms of investment in the acquired firm's R&D assets and for the local science and technology system.
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BACKGROUND: The quality standards of the Dutch Society of Intensive Care require monitoring of the satisfaction of patient's relatives with respect to care. Currently, no suitable instrument is available in the Netherlands to measure this. This study describes the development and psychometric evaluation of the questionnaire-based Consumer Quality Index 'Relatives in Intensive Care Unit' (CQI 'R-ICU'). The CQI 'R-ICU' measures the perceived quality of care from the perspective of patients' relatives, and identifies aspects of care that need improvement.METHODS: The CQI 'R-ICU' was developed using a mixed method design. Items were based on quality of care aspects from earlier studies and from focus group interviews with patients' relatives. The time period for the data collection of the psychometric evaluation was from October 2011 until July 2012. Relatives of adult intensive care patients in one university hospital and five general hospitals in the Netherlands were approached to participate. Psychometric evaluation included item analysis, inter-item analysis, and factor analysis.RESULTS: Twelve aspects were noted as being indicators of quality of care, and were subsequently selected for the questionnaire's vocabulary. The response rate of patients' relatives was 81% (n = 455). Quality of care was represented by two clusters, each showing a high reliability: 'Communication' (α = .80) and 'Participation' (α = .84). Relatives ranked the following aspects for quality of care as most important: no conflicting information, information from doctors and nurses is comprehensive, and health professionals take patients' relatives seriously. The least important care aspects were: need for contact with peers, nuisance, and contact with a spiritual counsellor. Aspects that needed the most urgent improvement (highest quality improvement scores) were: information about how relatives can contribute to the care of the patient, information about the use of meal-facilities in the hospital, and involvement in decision-making on the medical treatment of the patient.CONCLUSIONS: The CQI 'R-ICU' evaluates quality of care from the perspective of relatives of intensive care patients and provides practical information for quality assurance and improvement programs. The development and psychometric evaluation of the CQI 'R-ICU' led to a draft questionnaire, sufficient to justify further research into the reliability, validity, and the discriminative power of the questionnaire.
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Communicatieprofessionals geven aan dat organisaties geconfronteerd worden met een almaar complexere samenleving en daarmee het overzicht verloren hebben. Zo’n overzicht, een ‘360 graden blik’, is echter onontbeerlijk. Dit vooral, aldus diezelfde communicatieprofessionals, omdat dan eerder kan worden opgemerkt wanneer de legitimiteit van een organisatie ter discussie staat en zowel tijdiger als adequater gereageerd kan worden. Op dit moment is het echter nog zo dat een reactie pas op gang komt als zaken reeds in een gevorderd stadium verkeren. Onderstromen blijven onderbelicht, als ze niet al geheel onzichtbaar zijn. Een van de verklaringen hiervoor is de grote rol van sociale media in de publieke communicatie van dit moment. Die media produceren echter zoveel data dat communicatieprofessionals daartegenover machteloos staan. De enige oplossing is automatisering van de selectie en analyse van die data. Helaas is men er tot op heden nog niet in geslaagd een brug te slaan tussen het handwerk van de communicatieprofessional en de vele mogelijkheden van een datagedreven aanpak. Deze brug dan wel de vertaling van de huidige praktijk naar een hogere technisch niveau staat centraal in dit onderzoeksproject. Daarbij gaat het in het bijzonder om een vroegtijdige herkenning van potentiële issues, in het bijzonder met betrekking tot geruchtvorming en oproepen tot mobilisatie. Met discoursanalyse, AI en UX Design willen we interfaces ontwikkelen die zicht geven op die onderstromen. Daarbij worden transcripten van handmatig gecodeerde discoursanalytische datasets ingezet voor AI, in het bijzonder voor de clustering en classificatie van nieuwe data. Interactieve datavisualisaties maken die datasets vervolgens beter doorzoekbaar terwijl geautomatiseerde patroon-classificaties de communicatieprofessional in staat stellen sociale uitingen beter in te schatten. Aldus wordt richting gegeven aan handelingsperspectieven. Het onderzoek voorziet in de oplevering van een high fidelity ontwerp en een handleiding plus training waarmee analisten van newsrooms en communicatieprofessionals daadwerkelijk aan de slag kunnen gaan.
Betonprinten biedt veel nieuwe mogelijkheden op het gebied van productie en materiaal, maar vraagt van het MKB en startups flinke investeringen in kennis en middelen om er mee aan de slag te gaan. Met name slicer software, dat 3D modellen omzet naar printercode, vormt een bottleneck omdat deze alleen commercieel en printer-specifiek verkrijgbaar zijn. Saxion, Vertico en White Lioness willen in dit project de haalbaarheid van gratis open source slicer software die als cloud dienst wordt aangeboden onderzoeken. Deze oplossing maakt betonprinten bereikbaar voor meer innovatieve toepassingen vanuit MKB en startups, en vormt een platform voor het verzamelen en delen van kennis op het gebied van betonprinten.
Deploying robots from indoor to outdoor environments (vise versa) with stable and accurate localization is very important for companies to secure the utilization in industrial applications such as delivering harvested fruits from plantations, deploying/docking, navigating under solar panels, passing through tunnels/underpasses and parking in garages. This is because of the sudden changes in operational conditions such as receiving high/low-quality satellite signals, changing field of view, dealing with lighting conditions and addressing different velocities. We observed these limitations especially in indoor-outdoor transitions after conducting different projects with companies and obtaining inaccurate localization using individual Robotics Operating Systems (ROS2) modules. As there are rare commercial solutions for IO-transitions, AlFusIOn is a ROS2-based framework aims to fuse different sensing and data-interpretation techniques (LiDAR, Camera, IMU, GNSS-RTK, Wheel Odometry, Visual Odometry) to guarantee the redundancy and accuracy of the localization system. Moreover, maps will be integrated to robustify the performance and ensure safety by providing geometrical information about the transitioning structures. Furthermore, deep learning will be utilized to understand the operational conditions by labeling indoor and outdoor areas. This information will be encoded in maps to provide robots with expected operational conditions in advance and beyond the current sensing state. Accordingly, this self-awareness capability will be incorporated into the fusion process to control and switch between the localization techniques to achieve accurate and smooth IO-transitions, e.g., GNSS-RTK will be deactivated during the transition. As an urgent and unique demand to have an accurate and continuous IO-transition towards fully autonomous navigation/transportation, Saxion University and the proposal’s partners are determined to design a commercial and modular industrial-based localization system with robust performance, self-awareness about the localization capabilities and less human interference. Furthermore, AlFusIOn will intensively collaborate with MAPS (a RAAKPRO proposed by HAN University) to achieve accurate localization in outdoor environments.
Lectoraat, onderdeel van NHL Stenden Hogeschool
