It is a well-known fact that a good preparation in the pre-departure stage can maximize the chances of a succesful foreign experience. But what is meant by a good preparation? And what are the expected results of such a preparation? This course focuses in internship and study abroad (pre-departure) preparation. Its aim is to prepare you for the personal, professional and academic challenges of living and working abroad. The course will address awareness and purpose in the acquisition of attitude, knowledge and skills related to international competencies.
Loneliness among young adults is a growing concern worldwide, posing serious health risks. While the human ecological framework explains how various factors such as socio-demographic, social, and built environment characteristics can affect this feeling, still, relatively little is known about the effect of built environment characteristics on the feelings of loneliness that young people experience in their daily life activities. This research investigates the relationship between built environment characteristics and emotional state loneliness in young adults (aged 18–25) during their daily activities. Leveraging the Experience Sampling Method, we collected data from 43 participants for 393 personal experiences during daily activities across different environmental settings. The findings of a mixed-effects regression model reveal that built environment features significantly impact emotional state loneliness. Notably, activity location accessibility, social company during activities, and walking activities all contribute to reducing loneliness. These findings can inform urban planners and municipalities to implement interventions that support youngsters’ activities and positive experiences to enhance well-being and alleviate feelings of loneliness in young adults. Specific recommendations regarding the built environment are (1) to create spaces that are accessible, (2) create spaces that are especially accessible by foot, and (3) provide housing with shared facilities for young adults rather than apartments/studios.
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To enhance the training of sport psychology consultants, it is important to know which learning experiences are useful for which components of professional development. We interviewed 15 novice consultants on their learning experiences related to 13 different topics. Traditional learning experiences (e.g., courses, teachers) were related to the development of practical know-how. Learning from others (e.g., peers, colleagues) was related to professional development (i.e., dealing with issues, challenges, and dilemmas that occur in sport psychology practice). Practical experience and reflective activities were related to both know-how and professional development. These results can be used to shape effective sport psychology education.
The IMPULS-2020 project DIGIREAL (BUas, 2021) aims to significantly strengthen BUAS’ Research and Development (R&D) on Digital Realities for the benefit of innovation in our sectoral industries. The project will furthermore help BUas to position itself in the emerging innovation ecosystems on Human Interaction, AI and Interactive Technologies. The pandemic has had a tremendous negative impact on BUas industrial sectors of research: Tourism, Leisure and Events, Hospitality and Facility, Built Environment and Logistics. Our partner industries are in great need of innovative responses to the crises. Data, AI combined with Interactive and Immersive Technologies (Games, VR/AR) can provide a partial solution, in line with the key-enabling technologies of the Smart Industry agenda. DIGIREAL builds upon our well-established expertise and capacity in entertainment and serious games and digital media (VR/AR). It furthermore strengthens our initial plans to venture into Data and Applied AI. Digital Realities offer great opportunities for sectoral industry research and innovation, such as experience measurement in Leisure and Hospitality, data-driven decision-making for (sustainable) tourism, geo-data simulations for Logistics and Digital Twins for Spatial Planning. Although BUas already has successful R&D projects in these areas, the synergy can and should significantly be improved. We propose a coherent one-year Impuls funded package to develop (in 2021): 1. A multi-year R&D program on Digital Realities, that leads to, 2. Strategic R&D proposals, in particular a SPRONG/sleuteltechnologie proposal; 3. Partnerships in the regional and national innovation ecosystem, in particular Mind Labs and Data Development Lab (DDL); 4. A shared Digital Realities Lab infrastructure, in particular hardware/software/peopleware for Augmented and Mixed Reality; 5. Leadership, support and operational capacity to achieve and support the above. The proposal presents a work program and management structure, with external partners in an advisory role.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Our country contains a very dense and challenging transport and mobility system. National research agendas and roadmaps of multiple sectors such as HTSM, Logistics and Agri&food, promote vehicle automation as a means to increase transport safety and efficiency. SMEs applying vehicle automation require compliance to application/sector specific standards and legislation. A key aspect is the safety of the automated vehicle within its design domain, to be proven by manufacturers and assessed by authorities. The various standards and procedures show many similarities but also lead to significant differences in application experience and available safety related solutions. For example: Industrial AGVs (Automated Guided Vehicles) have been around for many years, while autonomous road vehicles are only found in limited testing environments and pilots. Companies are confronted with an increasing need to cover multiple application environments, such restricted areas and public roads, leading to complex technical choices and parallel certification/homologation procedures. SafeCLAI addresses this challenge by developing a framework for a generic safety layer in the control of autonomous vehicles that can be re-used in different applications across sectors. This is done by extensive consolidation and application of cross-sectoral knowledge and experience – including analysis of related standards and procedures. The framework promises shorter development times and enables more efficient assessment procedures. SafeCLAI will focus on low-speed applications since they are most wanted and technically best feasible. Nevertheless, higher speed aspects will be considered to allow for future extension. SafeCLAI will practically validate (parts) of the foreseen safety layer and publish the foreseen framework as a baseline for future R&D, allowing coverage of broader design domains. SafeCLAI will disseminate the results in the Dutch arena of autonomous vehicle development and application, and also integrate the project learnings into educational modules.
