Abstract
Rapidly changing environments place different players at the vortex of the innovation process. Therefore, in the digital age, strong businesses are sometimes built on perceptions and on the approval of the community. The shift from linear value chains to ecosystems is likely to occur in 4.0 organizations adopting service or customer orientations, according to their participation in networked ecosystems. Moving from organization-centered innovation to ecosystem co-creation will approach individuals and institutions thus enhancing sustainable and smart product development along with trust. Embedded innovation is a self-sustained process in which firms and stakeholders interact in a common environment creating a common identity. Empirical results reinforce the role of open innovation strategies and the user community as pillars of sustainable innovation ecosystems. Policy actions need to reinforce these ecosystems as they will generate employment encompassing innovative and inclusive growth, fostering the resilience of societies and environmental sustainability. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This article presents an innovative legged-wheeled system, designed to be applied in a hybrid robotic vehicle's locomotion system, as its driving member. The proposed system will be capable to combine the advantages of legged and wheeled locomotion systems, having 3DOF connected through a combination of both rigid and non-rigid joints. This configuration provides the vehicle the ability to absorb impacts and selected external disturbances. A state space approach was adopted to control the joints, increasing the system's stability and adaptability. Throughout this article, the entire design process of this robotic system will be presented, as well as its modeling and control. The proposed system's design is biologically inspired, having as reference the human leg, resulting in the development of a prototype. The results of the testing process with the proposed prototype are also presented. This system was designed to be modular, low-cost, and to increase the autonomy of typical autonomous legged-wheeled locomotion systems.

Abstract
Developed within the scope of a SciTech research project, this chapter records in a procedural way the design centred on senior users for a set of three serious games for the eHealth field, designated by the authors as “Carnival.” The chapter, having as its leitmotif the project aforementioned, looking at its motivation, breaks down the systems augmented feedback interfaces—BodyGrip and SHaRe—to evaluate, rehabilitate, and monitor dexterity and manual strength. Topics related to empathy and well-being in the user experience design process, namely guidelines for empathy in different project phases, participatory design, inclusiveness, and amusement are identified. Withal listed the development phases of three games dynamics inherent to the “Carnival” set —“High Striker,” “Claw Machine,” “HotDog Sauce”—punctuating with the discussion and contributions to the e-health area describing its potential for Evaluate, rehabilitate, and monitor dexterity and manual strength.

Abstract
When an oil spill occurs, a prompt response reduces significantly the impact. The preparedness and contingency plans are essential to identify the most appropriate technologies. Unmanned and autonomous vehicles (UAVs) is emerging as a powerful tool of strategic potential in the observation, oil tracking and damage assessment of an oil spill. The SpilLess project explored the suitability of these devices to be the first-line response to an oil spill. This work analyses the operational requirements related to environmental parameters following a two steps approach: 1) Environmental characterization from long wind and waves time series and modelling; 2) Definition of the optimal periods for operating each UAVs. We have defined the periods in which each of these facilities acts best, confirming that the operational limits of UAVs are not significantly more restrictive than the traditional operations. UAVs should be included in contingency plans as available tools to fight against oil spills.

Abstract
This work addresses a stochastic framework for optimal coordination of a microgrid-based virtual power plant (VPP) that participates in day-ahead energy and ancillary service markets. The microgrids are equipped with different types of distributed energy resources. A two-stage optimization formulation is proposed to maximize the benefit of the virtual power plant and minimize the energy procurement costs of the Distribution System Operator (DSO). The proposed model determines the optimal commitment scheduling of energy resources, considering the capacity withholding opportunities of the VPP that should be detected by the DSO. To evaluate the effectiveness of the proposed model, the algorithm is assessed for the 123-bus IEEE test system. The results show that the proposed method successfully maximizes the virtual power plant profit considering capacity withholding penalties.

Abstract
Considerable amounts of data are required for a deep learning model to generalize to unseen cases successfully. Furthermore, such data is often manually labeled, making its annotation process costly and time-consuming. We propose using unlabeled real-world data in conjunction with automatically labeled synthetic data, obtained from simulators, to surpass the increasing need for annotated data. By obtaining real counterparts of simulated samples using CycleGAN and subsequently performing fine-tuning with such samples, we manage to improve a vehicle part's detection system performance by 2.5%, compared to the baseline exclusively trained on simulated images. We explore adding a semantic consistency loss to CycleGAN by re-utilizing previous work's trained networks to regularize the conversion process. Moreover, the addition of a post-processing step, which we denominate global NMS, highlights our approach's effectiveness by better utilizing our detection model's predictions and ultimately improving the system's performance by 14.7%.