Purpose Hyper-collaboration means managing ecosystems not just as candy stores full of opportunities, but as fiercely competitive arenas in which companies fight for the best partners, technologies and networks to create, build and defend added value. Design/methodology/approach To address the challenges inherent in the dynamic world of hyper-collaboration, the authors identify the five pillars of ecosystem management and the priorities for implementing them. Findings To be successful, establish a clear vision and sense of purpose that guides the ecosystem evolution but is robust enough to deal with rapid changes. Practical implications Setting out clear IP principles is important, but it should be based on how best to maximize the overall value of the collaboration to all parties, rather than just protection. Originality/value Many of the world’s greatest technological challenges and opportunities, such as urbanization and mobility, are impossible to solve without forming a vast network of private and public organizations that work seamlessly together. Hyper-collaboration implies adopting a mindset that assumes it’s likely someone somewhere in the world already knows what you need to know to address such challenges– and it is unlikely that this person works in your company.

Heart diseases are the most important causes of death in the world and over the years, the study of cardiac movement has been carried out mainly in two dimensions, however, it is important to consider that the deformations due to the movement of the heart occur in a three-dimensional space. The 3 D + t analysis allows to describe most of the motions of the heart, for example, the twisting motion that takes place on every beat cycle that allows us identifying abnormalities of the heart walls. Therefore, it is necessary to develop algorithms that help specialists understand the cardiac movement. In this work, we developed a new approach to determine the cardiac movement in three dimensions using a differential optical flow approach in which we use the steered Hermite transform (SHT) which allows us to decompose cardiac volumes taking advantage of it as a model of the human vision system (HVS). Our proposal was tested in complete cardiac computed tomography (CT) volumes ( 3 D + t ), as well as its respective left ventricular segmentation. The robustness to noise was tested with good results. The evaluation of the results was carried out through errors in forwarding reconstruction, from the volume at time t to time t + 1 using the optical flow obtained (interpolation errors). The parameters were tuned extensively. In the case of the 2D algorithm, the interpolation errors and normalized interpolation errors are very close and below the values reported in ground truth flows. In the case of the 3D algorithm, the results were compared with another similar method in 3D and the interpolation errors remained below 0.1. These results of interpolation errors for complete cardiac volumes and the left ventricle are shown graphically for clarity. Finally, a series of graphs are observed where the characteristic of contraction and dilation of the left ventricle is evident through the representation of the 3D optical flow.

No abstract