The aim of the current study was twofold: (1) to validate the use of action sport cameras for quantifying focus of visual attention in sailing and (2) to apply this method to examine whether an external focus of attention is associated with better performance in upwind sailing. To test the validity of this novel quantification method, we first calculated the agreement between gaze location measures and head orientation measures in 13 sailors sailing upwind during training regattas using a head mounted eye tracker. The results confirmed that for measuring visual focus of attention in upwind sailing, the agreement for the two measures was high (intraclass correlation coefficient (ICC) = 0.97) and the 95% limits of agreement were acceptable (between -8.0% and 14.6%). In a next step, we quantified the focus of visual attention in sailing upwind as fast as possible by means of an action sport camera. We captured sailing performance, operationalised as boat speed in the direction of the wind, and environmental conditions using a GPS, compass and wind meter. Four trials, each lasting 1 min, were analysed for 15 sailors each, resulting in a total of 30 upwind speed trials on port tack and 30 upwind speed trials on starboard tack. The results revealed that in sailing - within constantly changing environments - the focus of attention is not a significant predictor for better upwind sailing performances. This implicates that neither external nor internal foci of attention was per se correlated with better performances. Rather, relatively large interindividual differences seem to indicate that different visual attention strategies can lead to similar performance outcomes.
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A culture change within an organization may be of importance in this turbulent world. An assessment of the current and desired cultural profiles can help estimate as to whether any changes are required. In this study the organizational culture of a housing association was examined from both the staff’s and external stakeholders’ perspectives. How does the current culture compare with the desired culture? Do the external stakeholders perceive the organization’s culture in a similar way? Do the staff’s and external stakeholders’ perceptions coincide with the organization’s intended image? The results demonstrate that the external stakeholders’ perceptions of the organizational culture in this case study are similar to those of the organization’s staff.
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It is unknown how movement patterns that are learned carry over to the field. The objective was to deter- mine whether training during a jump-landing task would transfer to lower extremity kinematics and kinetics during sidestep cutting.Methods Forty healthy athletes were assigned to the ver- bal internal focus (IF, n = 10), verbal external focus (EF, n = 10), video (VI, n = 10) or control (CTRL, n = 10) group. A jump-landing task was performed as baseline followed by training blocks (TR1 and TR2) and a post-test. Group-spe- cific instructions were given in TR1 and TR2. In addition, participants in the IF, EF and VI groups were free to ask for feedback after every jump during TR1 and TR2. Retention was tested after 1 week. Transfer of learned skill was deter- mined by having participants perform a 45° unanticipated sidestep cutting task. 3D hip, knee and ankle kinematics and kinetics were the main outcome measures.Results During sidestep cutting, the VI group showed greater hip flexion ROM compared to the EF and IF groups (p < 0.001). The EF (p < 0.036) and VI (p < 0.004) groups had greater knee flexion ROM compared to the IF group. Conclusions Improved jump-landing technique car- ried over to sidestep cutting when stimulating an external attentional focus combined with self-controlled feedback. Transfer to more sport-specific skills may demonstrate potential to reduce injuries on the field. Clinicians and practitioners are encouraged to apply instructions that stimulate an external focus of attention, of which visual instructions seem to be very powerful.
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The objective of DIGIREAL-XL is to build a Research, Development & Innovation (RD&I) Center (SPRONG GROUP, level 4) on Digital Realities (DR) for Societal-Economic Impact. DR are intelligent, interactive, and immersive digital environments that seamlessly integrate Data, Artificial Intelligence/Machine Learning, Modelling-Simulation, and Visualization by using Game and Media Technologies (Game platforms/VR/AR/MR). Examples of these DR disruptive innovations can be seen in many domains, such as in the entertainment and service industries (Digital Humans); in the entertainment, leisure, learning, and culture domain (Virtual Museums and Music festivals) and within the decision making and spatial planning domain (Digital Twins). There are many well-recognized innovations in each of the enabling technologies (Data, AI,V/AR). However, DIGIREAL-XL goes beyond these disconnected state-of-the-art developments and technologies in its focus on DR as an integrated socio-technical concept. This requires pre-commercial, interdisciplinary RD&I, in cross-sectoral and inter-organizational networks. There is a need for integrating theories, methodologies, smart tools, and cross-disciplinary field labs for the effective and efficient design and production of DR. In doing so, DIGIREAL-XL addresses the challenges formulated under the KIA-Enabling Technologies / Key Methodologies for sectoral and societal transformation. BUas (lead partner) and FONTYS built a SPRONG group level 4 based on four pillars: RD&I-Program, Field Labs, Lab-Infrastructure, and Organizational Excellence Program. This provides a solid foundation to initiate and execute challenging, externally funded RD&I projects with partners in SPRONG stage one ('21-'25) and beyond (until' 29). DIGIREAL-XL is organized in a coherent set of Work Packages with clear objectives, tasks, deliverables, and milestones. The SPRONG group is well-positioned within the emerging MINDLABS Interactive Technologies eco-system and strengthens the regional (North-Brabant) digitalization agenda. Field labs on DR work with support and co-funding by many network organizations such as Digishape and Chronosphere and public, private, and societal organizations.
The objective of DIGIREAL-XL is to build a Research, Development & Innovation (RD&I) Center (SPRONG GROUP, level 4) onDigital Realities (DR) for Societal-Economic Impact. DR are intelligent, interactive, and immersive digital environments thatseamlessly integrate Data, Artificial Intelligence/Machine Learning, Modelling-Simulation, and Visualization by using Gameand Media Technologies (Game platforms/VR/AR/MR). Examples of these DR disruptive innovations can be seen in manydomains, such as in the entertainment and service industries (Digital Humans); in the entertainment, leisure, learning, andculture domain (Virtual Museums and Music festivals) and within the decision making and spatial planning domain (DigitalTwins). There are many well-recognized innovations in each of the enabling technologies (Data, AI,V/AR). However, DIGIREAL-XL goes beyond these disconnected state-of-the-art developments and technologies in its focus on DR as an integrated socio-technical concept. This requires pre-commercial, interdisciplinary RD&I, in cross-sectoral andinter-organizational networks. There is a need for integrating theories, methodologies, smart tools, and cross-disciplinaryfield labs for the effective and efficient design and production of DR. In doing so, DIGIREAL-XL addresses the challengesformulated under the KIA-Enabling Technologies / Key Methodologies for sectoral and societal transformation. BUas (lead partner) and FONTYS built a SPRONG group level 4 based on four pillars: RD&I-Program, Field Labs, Lab-Infrastructure, and Organizational Excellence Program. This provides a solid foundation to initiate and execute challenging, externally funded RD&I projects with partners in SPRONG stage one ('21-'25) and beyond (until' 29). DIGIREAL-XL is organized in a coherent set of Work Packages with clear objectives, tasks, deliverables, and milestones. The SPRONG group is well-positioned within the emerging MINDLABS Interactive Technologies eco-system and strengthens the regional (North-Brabant) digitalization agenda. Field labs on DR work with support and co-funding by many network organizations such as Digishape and Chronosphere and public, private, and societal organizations
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.
