The Netherlands Research School for Astronomy (NOVA) has operated a Mobile Planetarium for over 14 years. Between 2009-2023, the project reached more than 400,000 learners and their teachers across the Netherlands. The project has been popular with schools since the beginning but continues to grow and reach increasing numbers of learners and schools each year. A project like the Mobile Planetarium does not continue growing this way without developing key ingredients or best practices. In this article, we describe the NOVA Mobile Planetarium project in detail and the challenges faced over the last 14 years. Reflection on the different aspects of the project has led to 10 best practices which have been critical to the continued success of this project. In this article, we aim to share our experiences to help other mobile planetarium projects around the world.
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BACKGROUND: The number of mobile apps that support smoking cessation is growing, indicating the potential of the mobile phone as a means to support cessation. Knowledge about the potential end users for cessation apps results in suggestions to target potential user groups in a dissemination strategy, leading to a possible increase in the satisfaction and adherence of cessation apps.OBJECTIVE: This study aimed to characterize potential end users for a specific mobile health (mHealth) smoking cessation app.METHODS: A quantitative study was conducted among 955 Dutch smokers and ex-smokers. The respondents were primarily recruited from addiction care facilities and hospitals through Web-based media via websites and forums. The respondents were surveyed on their demographics, smoking behavior, and personal innovativeness. The intention to use and the attitude toward a cessation app were determined on a 5-point Likert scale. To study the association between the characteristics and intention to use and attitude, univariate and multivariate ordinal logistic regression analyses were performed.RESULTS: The multivariate ordinal logistic regression showed that the number of previous quit attempts (odds ratio [OR] 4.1, 95% CI 2.4-7.0, and OR 3.5, 95% CI 2.0-5.9) and the score on the Fagerstrom Test of Nicotine Dependence (OR 0.8, 95% CI 0.8-0.9, and OR 0.8, 95% CI 0.8-0.9) positively correlates with the intention to use a cessation app and the attitude toward cessation apps, respectively. Personal innovativeness also positively correlates with the intention to use (OR 0.3, 95% CI 0.2-0.4) and the attitude towards (OR 0.2, 95% CI 0.1-0.4) a cessation app. No associations between demographics and the intention to use or the attitude toward using a cessation app were observed.CONCLUSIONS: This study is among the first to show that demographic characteristics such as age and level of education are not associated with the intention to use and the attitude toward using a cessation app when characteristics related specifically to the app, such as nicotine dependency and the number of quit attempts, are present in a multivariate regression model. This study shows that the use of mHealth apps depends on characteristics related to the content of the app rather than general user characteristics.
In mobile robotics, LASER scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due to their accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for the identification and localization of objects using 2D LiDARs. Our main contribution lies in formally defining LASER sensor measurements and their representation, the identification of objects, their main properties, and their location in a scene. We validate our proposal with experiments in generic semi-structured environments common in autonomous navigation, and we demonstrate its feasibility in multiple object detection and identification, strictly following its analytical representation. Finally, our proposal further encourages and facilitates the design, modeling, and implementation of other applications that use LASER scanners as a distance sensor.
The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
Een goede voorbereiding is het halve werk, ook voor patiënten op de wachtlijst voor een chirurgische ingreep. We onderzoeken hoe de e-health-applicatie 'Beter Voorbereid' mensen helpt om sterker aan de start van een operatie te staan en zo sneller te herstellen.
Veel patiënten binnen de GGZ kampen met chronische pijn en depressie. Het bevorderen van een gezond beweegpatroon speelt een belangrijke rol in hun behandeling. Deze patiënten kunnen echter door emoties en veranderde prikkelverwerking signalen van het lichaam niet goed inschatten. Daarbij zijn hun klachten belemmerend in hun activiteiten waardoor motivatie vaak afwezig is. GGZ-professionals gebruiken zorgstandaarden waarbij uitgegaan wordt van 'one-size-fits-all' behandelprogramma's. Deze sluiten onvoldoende aan bij de behoefte aan gepersonaliseerde interventies uitgaande van zelfmanagement van de individuele patiënt. Dit pleit voor een instrument dat professionals helpt objectief inzicht te krijgen in het beweegpatroon van hun patiënten, dat gepersonaliseerde feedback geeft en ondersteunt bij de verdere individueel passende begeleiding van de patiënt. Zelfmeettechnologie ('activity trackers') lijkt hier goed te passen. De mogelijkheden om zelfmeettechnologie als basis voor de behandeling van deze patiënten te gebruiken zijn echter bij GGZ-professionals veelal onbekend. Daarnaast is het inzetten van alleen zelfmeettechnologie waarschijnlijk onvoldoende en is niet goed bekend hoe deze patiënten gemotiveerd kunnen worden om deze technologie te (blijven) gebruiken. In dit project willen de Hanzehogeschool Groningen, Inter-Psy, Transcare en MobileCare samen met professionals en patiënten en andere nog te betrekken partners (o.a. het Rob Giel Onderzoekscentrum als trekker van het eHealth netwerk Noord-Nederland heeft aangegeven een bijdrage te willen leveren) ontdekken hoe op een goede manier aan de bovenbeschreven behoefte van GGZ-professionals kan worden bijgedragen. Beoogd wordt om met deze subsidie een proof of concept te leveren van een digitaal instrument dat op basis van zelfmeettechnologie meerwaarde biedt in de behandeling van patiënten met chronische pijn en depressie. Deze proof of concept vormt de basis voor een te schrijven subsidievoorstel om dit verder te ontwikkelen.
