The new partner (UNG) will bring fresh perspectives as well as invaluable expertise in device modelling and charge transport characterization that will significantly enhance the progress of the project. Their deep knowledge of advanced experimental characterization methods will enable us to explore innovative, environmentally friendly materials that are critical for our organic transistors. Moreover, they will provide essential information on the intrinsic material properties, such as charge transport characteristics and ferroelectric behaviour, which are vital for optimizing device performance in terms of response, stability and reproducibility. By collaborating closely, we will be able to leverage their cutting-edge research methodologies and access to state-of-the-art facilities, fostering a dynamic exchange of ideas that will accelerate our development timeline and secure the successful progress of our project. Developing a theoretical model for organic transistors with ferroelectric interlayers is set to surpass current knowledge by providing creative insights into charge transport and nonlinear dynamics in these systems. This innovative framework will drive experimental research in leveraging novel, environmentally friendly materials that minimize ecological impact while optimizing device performance. By facilitating the design of advanced multimodal applications, such as next-generation pressure sensing and mimicking synapses, this model will significantly advance the design of fabrication of artificial sensory neuron within this project. Broader, the model will advance development of devices that emulate biological processes. Additionally, it will encourage interdisciplinary collaboration among material scientists and computational physicists, enrich educational resources, and equip the next generation of researchers with cutting-edge skills, ultimately inspiring transformative concepts that could redefine the future of sustainable technologies.

The replication of the circle of information coming from the environment, to the skin, to an action mediated by the brain, requires a lot of advances in smart technology and materials development. Embedding sensors in smart architectures that record the stimulus from the environment and transform it into action is the objective of artificial skins. At the moment, different sensors have to be implemented in the artificial skin matrix for each stimulus. The goal of this project is to develop a single multi-stimuli responsive material, which would allow a simplification of the artificial skin and enable unprecedented spatial resolution. The material will be comprised of a smart core, responsive to temperature and humidity, and a piezoelectric shell for pressure sensing. The swelling of the smart core upon stimuli will be sensed by the piezoelectric shell and produce a measurable potential. This architecture will be achieved thanks to the use of novel vapor-based technologies for material processing that allow fabrication at the nanoscale. The advantage of using a dry, vapor-based, polymerization for the smart core is that it will be possible to cumulate different functionalities and engineered composition gradients, which are difficult to obtain by conventional synthesis. Nano-structuration of such materials in core-shell site-specific arrays will allow to create a sensing network with spatial resolution down to 1mm and lower. The network will respond to the stimuli coming from the environment and recognize them in terms of location and type of stimuli. The successful execution of the SmartCore project will have a strong impact in the design and production of future structures, with consequences in sensoring, biotechnology and tissue engineering.

Industry foresight for next 10 years are highlighting how the global economy will be fully globalized and competition for markets will become fiercer. New industrial players from emerging and newly emerged industrial economies will fight for market share with multi-nationals and companies from traditionally industrialized countries, including the EU. This will drive companies seeking for innovation to develop a competitive edge for new products and services, and to adopt operations that are more efficient. Europe is a global leader in supplying advanced manufacturing technologies, however notwithstanding the relevant investment in R&D to develop such technologies, the effective uptake is still weak and European companies are lagging behind in comparison to global competitors regarding demand and use of them. Despite the benefits AMTs offer, the majority of manufacturing SMEs in the EU are not yet using these technologies. One of the key obstacles to AMT adoption is represented by the general weakness of the support system supplying with adequate capacities in all areas of advanced manufacturing sector. The overall project objective of the project is to improve the Advanced Manufacturing Ecosystem for European SMEs by reinforcing the innovation infrastructures that should provide support to manufacturing SME. The aim is thus to collaboratively address a common innovation support challenge consisting in providing adequate support to SMEs in the adoption of AMT. The Peer Learning activity is expected to collect and identify a wide range of services that Innovation intermediary agencies may provide to SMEs to stimulate their transformation through the AMT uptake. The expected Design Options Paper will serve as a “guide” or a “handbook” to other agencies and business support centers to provide more focused support services and to set up “Regional Innovation Hubs” to improve a suitable ecosystem fostering AMT uptake.

The media production industry is rapidly evolving and expanding globally, primarily driven by the explosion in mobile devices such as phones and tablets, the ubiquity of internet access and the subsequent demand to consume content anywhere, at any time and on any device. Modern media production workflows must thus be agile and support multiple heterogeneous sources and playout targets in a responsive and cost-effective way. This poses challenges for quality assurance and Media Asset Management (MAM) where access to rich metadata and data integrity are essential. Today, these challenges can only be addressed by enterprise and high-end solutions used by major public and private broadcast companies; solutions which are highly proprietary, extremely costly and complex to implement for the large number of small to medium sized creative companies, small broadcasters and corporate teams responsible for producing audiovisual content for broadcast, video on demand, web and mobile consumption. This proposal aims to take recent technology research results from automatic real-time content analysis and processing and bring-to-market an affordable, integrated, scalable, open commercial software solution. It will include the automated extraction of metadata (e.g., detection of logos, faces, temporal segmentation, visual similarity) for use in media asset management and the analysis of the visual quality of the content and automatic tools for reducing or repairing quality impairments. The algorithms will be implemented as modules that can be integrated into real-time analysis pipelines, as well as batch processing for non-real time workflows and can be deployed locally or scaled as cloud-based services.