Background: Alcohol use is associated with an automatic tendency to approach alcohol, and the retraining of this tendency (cognitive bias modification [CBM]) shows therapeutic promise in clinical settings. To improve access to training and to enhance participant engagement, a mobile version of alcohol avoidance training was developed.Objective: The aims of this pilot study were to assess (1) adherence to a mobile health (mHealth) app; (2) changes in weekly alcohol use from before to after training; and (3) user experience with regard to the mHealth app.Methods: A self-selected nonclinical sample of 1082 participants, who were experiencing problems associated with alcohol, signed up to use the alcohol avoidance training app Breindebaas for 3 weeks with at least two training sessions per week. In each training session, 100 pictures (50 of alcoholic beverages and 50 of nonalcoholic beverages) were presented consecutively in a random order at the center of a touchscreen. Alcoholic beverages were swiped upward (away from the body), whereas nonalcoholic beverages were swiped downward (toward the body). During approach responses, the picture size increased to mimic an approach movement, and conversely, during avoidance responses, the picture size decreased to mimic avoidance. At baseline, we assessed sociodemographic characteristics, alcohol consumption, alcohol-related problems, use of other substances, self-efficacy, and craving. After 3 weeks, 37.89% (410/1082) of the participants (posttest responders) completed an online questionnaire evaluating adherence, alcohol consumption, and user satisfaction. Three months later, 19.03% (206/1082) of the participants (follow-up responders) filled in a follow-up questionnaire examining adherence and alcohol consumption.Results: The 410 posttest responders were older, were more commonly female, and had a higher education as compared with posttest dropouts. Among those who completed the study, 79.0% (324/410) were considered adherent as they completed four or more sessions, whereas 58.0% (238/410) performed the advised six or more training sessions. The study identified a significant reduction in alcohol consumption of 7.8 units per week after 3 weeks (95% CI 6.2-9.4, P<.001; n=410) and another reduction of 6.2 units at 3 months for follow-up responders (95% CI 3.7-8.7, P<.001; n=206). Posttest responders provided positive feedback regarding the fast-working, simple, and user-friendly design of the app. Almost half of the posttest responders reported gaining more control over their alcohol use. The repetitious and nonpersonalized nature of the intervention was suggested as a point for improvement.Conclusions: This is one of the first studies to employ alcohol avoidance training in a mobile app for problem drinkers. Preliminary findings suggest that a mobile CBM app fulfils a need for problem drinkers and may contribute to a reduction in alcohol use. Replicating these findings in a controlled study is warranted.
The inefficiency of maintaining static and long-lasting safety zones in environments where actual risks are limited is likely to increase in the coming decades, as autonomous systems become more common and human workers fewer in numbers. Nevertheless, an uncompromising approach to safety remains paramount, requiring the introduction of novel methods that are simultaneously more flexible and capable of delivering the same level of protection against potentially hazardous situations. We present such a method to create dynamic safety zones, the boundaries of which can be redrawn in real-time, taking into account explicit positioning data when available and using conservative extrapolation from last known location when information is missing or unreliable. Simulation and statistical methods were used to investigate performance gains compared to static safety zones. The use of a more advanced probabilistic framework to further improve flexibility is also discussed, although its implementation would not offer the same level of protection and is currently not recommended.
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Rapport in opdracht van de gemeente Groningen. Aanleiding voor het schrijven van dit rapport is dat de gemeente Groningen graag wil weten hoe ze met het gebruik van open data het (openbaar) vervoer voor studenten in en naar de stad Groningen kunnen verbeteren. Het onderzoek richtte zich daarom op studenten. In het advies wordt genoemd dat:* Om de studenten van dienst te zijn kan men een applicatie voor smartphone en tablet of een mobiele website ontwikkelen. * Unique selling point is een applicatie waarmee men in één applicatie alle informatie heeft. * Persoonlijk hulpmiddel door een applicatie te laten instellen op persoonlijke voorkeur/routes * Informatie over de drukte in trein ligt bij de Provincie Groningen. * Ontbrekende data over het fietsverkeer kan men verkrijgen met telpunten van de gemeente. This (research) activity is a pilot of Opening Up and has been co-funded by the Interreg IVB North Sea Region Programme
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
Structural colour (SC) is created by light interacting with regular nanostructures in angle-dependent ways resulting in vivid hues. This form of intense colouration offers commercial and industrial benefits over dyes and other pigments. Advantages include durability, efficient use of light, anti-fade properties and the potential to be created from low cost materials (e.g. cellulose fibres). SC is widely found in nature, examples include butterflies, squid, beetles, plants and even bacteria. Flavobacterium IR1 is a Gram-negative, gliding bacterium isolated from Rotterdam harbour. IR1 is able to rapidly self-assemble into a 2D photonic crystal (a form of SC) on hydrated surfaces. Colonies of IR1 are able to display intense, angle-dependent colours when illuminated with white light. The process of assembly from a disordered structure to intense hues, that reflect the ordering of the cells, is possible within 10-20 minutes. This bacterium can be stored long-term by freeze drying and then rapidly activated by hydration. We see these properties as suiting a cellular reporter system quite distinct from those on the market, SC is intended to be “the new Green Fluorescent Protein”. The ability to understand the genomics and genetics of SC is the unique selling point to be exploited in product development. We propose exploiting SC in IR1 to create microbial biosensors to detect, in the first instance, volatile compounds that are damaging to health and the environment over the long term. Examples include petroleum or plastic derivatives that cause cancer, birth defects and allergies, indicate explosives or other insidious hazards. Hoekmine, working with staff and students within the Hogeschool Utrecht and iLab, has developed the tools to do these tasks. We intend to create a freeze-dried disposable product (disposables) that, when rehydrated, allow IR1 strains to sense and report multiple hazardous vapours alerting industries and individuals to threats. The data, visible as brightly coloured patches of bacteria, will be captured and quantified by mobile phone creating a system that can be used in any location by any user without prior training. Access to advice, assay results and other information will be via a custom designed APP. This work will be performed in parallel with the creation of a business plan and market/IP investigation to prepare the ground for seed investment. The vision is to make a widely usable series of tests to allow robust environmental monitoring for all to improve the quality of life. In the future, this technology will be applied to other areas of diagnostics.
A world where technology is ubiquitous and embedded in our daily lives is becoming increasingly likely. To prepare our students to live and work in such a future, we propose to turn Saxion’s Epy-Drost building into a living lab environment. This will entail setting up and drafting the proper infrastructure and agreements to collect people’s location and building data (e.g. temperature, humidity) in Epy-Drost, and making the data appropriately available to student and research projects within Saxion. With regards to this project’s effect on education, we envision the proposal of several derived student projects which will provide students the opportunity to work with huge amounts of data and state-of-the-art natural interaction interfaces. Through these projects, students will acquire skills and knowledge that are necessary in the current and future labor-market, as well as get experience in working with topics of great importance now and in the near future. This is not only aligned with the Creative Media and Game Technologies (CMGT) study program’s new vision and focus on interactive technology, but also with many other education programs within Saxion. In terms of research, the candidate Postdoc will study if and how the data, together with the building’s infrastructure, can be leveraged to promote healthy behavior through playful strategies. In other words, whether we can persuade people in the building to be more physically active and engage more in social interactions through data-based gamification and building actuation. This fits very well with the Ambient Intelligence (AmI) research group’s agenda in Augmented Interaction, and CMGT’s User Experience line. Overall, this project will help spark and solidify lasting collaboration links between AmI and CMGT, give body to AmI’s new Augmented Interaction line, and increase Saxion’s level of education through the dissemination of knowledge between researchers, teachers and students.
