Wednesday 17th of January MediaLAB Amsterdam arranged LABFEST, a final expo where we could showcase our protypes and talk to people in the industry about our projects. We got a lot of nice feedback and are happy with the end-product we came up with. Quite a lot of people showed up and we were excited to talk to people about our prototype and the future possibilities of our Virtual Reality Exposure Therapy!
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Wat zijn belangrijke succesfactoren om onderzoek, onderwijs en ondernemen bij elkaar te brengen, zó dat 'het klikt'. De uitdaging voor de toekomst van bedrijven in de smart factoryligt bij data science: het omzetten van ruwe (sensor) data naar (zinnige) informatie en kennis, waarmee producten en diensten verbeterd kunnen worden. Tevens programma van het symposium t.g.l. inauguratie 3 december 2015
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Background: Despite high prevalence of mental problems among elderly migrants in The Netherlands, the use of psychosocial care services by this group is low. Scientific evidence points at the crucial role of social support for mental health and the use of psychosocial services. We therefore explored the role of social networks in the access to psychosocial care among elderly migrants in The Netherlands. Methods: A qualitative study was conducted using semi-structured group interviews and individual interviews. The eight group and eleven individual interviews (respectively n = 58 and n = 11) were conducted in The Netherlands with Turkish, Moroccan, Surinamese, and Dutch elderly. The data were analysed through coding and comparing fragments and recognizing patterns. Results: Support of the social network is important to navigate to psychosocial care and is most frequently provided by children. However, the social network of elderly migrants is generally not able to meet the needs of the elderly. This is mostly due to poor mental health literacy of the social network, taboo, and stigma around mental illness and the busy lives of the social network members. Conclusions: Strategies to address help-seeking barriers should consider mental health literacy in elderly migrants as well as their social networks, and counteract taboos and stigma of mental health problems.
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The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
