Many cities around the world are aspiring to become. However, smart initiatives often give little weight to the opinions of average citizens. Social media are one of the most important sources of citizen opinions. This paper presents a prototype of a framework for processing social media posts with municipal decision-making in mind. The framework consists of a sequence of three steps: (1) determining the sentiment polarity of each social media post (2) identifying prevalent topics and mapping these topics to individual posts, and (3) aggregating these two pieces of information into a fuzzy number representing the overall sentiment expressed towards each topic. Optionally, the fuzzy number can be reduced into a tuple of two real numbers indicating the "amount" of positive and negative opinion expressed towards each topic. The framework is demonstrated on tweets published from Ostrava, Czechia over a period of about two months. This application illustrates how fuzzy numbers represent sentiment in a richer way and capture the diversity of opinions expressed on social media.

In the Czech Republic, the initial network of Digital Innovation Hubs (DIHs) in 2016 was very small – it included only two pioneer hubs raised from Horizon 2020 European projects focused on support of digital manufacturing (DIGIMAT located in Kuřim was the first one) and high-performance computing (the National Supercomputing Centre IT4Innovations located in Ostrava was the first DIH registered in CZ). Since then, the Czech network has enlarged up to twelve DIHs and their specialisation varies from manufacturing, cybersecurity, artificial intelligence, robotics, high-performance computing up to sectoral focus such as agriculture and food industry, health or smart cities and smart regions development. How are the Czech DIHs built and structured? They are based often on a leading competence centre, research organisation, technical university, science park, technology centre or the most active innovations-supporting NGOs who have created an efficient consortium of other partners around them, always following the selected field of specialisation. The DIHs in Czechia, as well as all over the Europe, have not been built "on a green meadow" – as we say in Czech, but based on a record of projects, activities and common references that have created the optimal mixture of the DIH s expertise and capacities.

In the scope of a future-oriented collaboration in the field of industrial production, the Fraunhofer-Gesellschaft is cooperating with the VSB – Technical University of Ostrava (VSB-TUO). The partners research and develop the potential offered by energy management technologies, artificial intelligence (AI) and intelligent production in industry. The collaboration provides production companies with innovative solutions, which they can in turn use to develop innovative and sustainable solutions for reducing greenhouse gas emissions. This builds on over five years of successful collaboration between the Fraunhofer Institute for Machine Tools and Forming Technology IWU, the Fraunhofer Institute for Chemical Technology ICT and the VSB – Technical University of Ostrava (VSB-TUO). The ambitious venture "Fraunhofer Innovation Platform for Applied Artificial Intelligence for Materials & Manufacturing at VSB – Technical University of Ostrava FIP-AI@VSB-TUO" commenced operation on June 1, 2021.

In another step towards autonomous driving, Skoda Auto has partnered with VSB - Technical University of Ostrava, Czech Republic, to develop new technologies for driving assistance systems. The collaboration between the two parties involve a'Follow the Vehicle' project that aims to have autonomous cars follow a manned lead vehicle. The technology, currently being tested on two correspondingly configured Skoda Superb iVs, has potential for car-sharing service providers, car rental companies or fleet operators. The'Follow the Vehicle' project follows the principle of'two cars, one driver' where the lead vehicle is driven by a human, determining route, speed, lane and other parameters. The autonomous car follows the lead vehicle at a distance of up to ten metres.

Today,insulated overhead conductors are increasingly used in many places of the world due to the higher operational reliability, elimination of phase-to-phase contact, closer distances between phases and stronger protection for animals. However, the standard protection devices are often not able to detect the conductor phase-to-ground fault and the more frequent tree/tree branch hitting conductor events as these events only lead to partial discharge (PD) activities instead of causing overcurrent seen on bare conductors. To solve this problem, in recent years, Technical University of Ostrava (VSB) devised a special meter to measure the voltage signal of the stray electrical field along the insulated overhead conductors, hoping to detect the above hazardous PD activities. In 2018, VSB published a large amount of waveform data recorded by their meter on Kaggle, the world's largest data science collaboration platform, looking for promising pattern recognition methods for this application. To tackle this challenge, we developed a unique method based on Seasonal and Trend decomposition using Loess (STL) and Support Vector Machine (SVM) to recognize PD activities on insulated overhead conductors. Different SVM kernels were tested and compared. Satisfactory classification rates on VSB dataset were achieved with the use of Gaussian radial basis kernel.