Open Access

The intention of the paper is to consider the role of technologies used to overcome the COVID-19’s challenges in higher education. On investigating, it has been found that the digital strategies utilizing innovation technologies help students to adapt to pandemic consequences. This study enlightens the various technologies that assist systems of healthcare and education in diverse aspects to outlive against COVID-19. Furthermore, the technological shift of world economy that happened during the pandemic and its influence in the society is discussed. Besides the considered innovation technologies, this paper also deals with changes that have taken place in education system of Kazakhstan in pandemic circumstances. Alongside our suggested solution for further educational process issues is also presented. A detailed review of the literature is done on COVID-19, digital technologies in education, blended learning using appropriate keywords on SCOPUS, Springer, Science Direct, and Google Scholar. To get insights some relevant sites and blogs are also taken into account. We have identified technologies used in Kazakhstan that play an important role now. There have been chosen exploratory and conclusive research design. Fifty-nine students provided qualitative responses; ninety-eight students and graduates participated in quantitative survey. The results of study has shown students’ increased awareness concerning blended learning. The scientific novelty lies in the description of the experience gained by higher schools during the quarantine. We believe that in the future, blended learning should be used largely. The innovations in the learning process cover two areas. The first is related to software used. It is like creation of a virtual university that facilitates communication between students and teachers. The second area correlates with content, without which any innovation will fail.

Production companies are required to implement efficient, flexible manufacturing processes to face the competitive global market. The ongoing digital transformation leads many companies to store production process data, which offers optimization potential using Artificial Intelligence (AI). However, production planning and control is often still performed manually with a significant amount of human expert knowledge. To enhance the decision-makers’ knowledge and hereby the decisions’ quality, the available database can be analyzed with AI methods. This paper aims at improving the human decision-making process by providing a methodology to evaluate and apply AI-generated knowledge w.r.t. its quality and long-term usefulness for decision-makers.

Hypotrochoidal profile contours have been produced in industrial applications in recent years using two-spindle processes, and they are considered effective high-quality solutions for form-fit shaft and hub connections. This study mainly concerns analytical approaches to determine the stresses and deformations in hypotrochoidal profile shafts due to pure bending loads. The formulation was developed according to bending principles using the mathematical theory of elasticity and conformal mappings. The loading was further used to investigate the rotating bending behaviour. The stress factors for the classical calculation of maximum bending stresses were also determined for all those profiles presented and compiled in the German standard DIN3689-1 for practical applications. The results were also compared with the corresponding numerical and experimental results, and very good agreement was observed. Additionally, based on previous work, the stress factor was determined for the case of torsional loading to calculate the maximum torsional stresses in the standardised profiles, and the results are listed in a table. This study contributes to the further refinement of the current DIN3689 standard.

Model predictive control (MPC) is a promising approach to the lateral and longitudinal control of autonomous vehicles. However, the parameterization of the MPC with respect to high-level requirements such as passenger comfort, as well as lateral and longitudinal tracking, is challenging. Numerous tuning parameters and conflicting requirements need to be considered. In this paper, we formulate the MPC tuning task as a multi-objective optimization problem. Its solution is demanding for two reasons: First, MPC-parameterizations are evaluated in a computationally expensive simulation environment. As a result, the optimization algorithm needs to be as sample-efficient as possible. Second, for some poor parameterizations, the simulation cannot be completed; therefore, useful objective function values are not available (for instance, learning with crash constraints). In this work, we compare the sample efficiency of multi-objective particle swarm optimization (MOPSO), a genetic algorithm (NSGA-II), and multiple versions of Bayesian optimization (BO). We extend BO by introducing an adaptive batch size to limit the computational overhead. In addition, we devise a method to deal with crash constraints. The results show that BO works best for a small budget, NSGA-II is best for medium budgets, and none of the evaluated optimizers are superior to random search for large budgets. Both proposed BO extensions are, therefore, shown to be beneficial.

Auf digitalen Märkten besteht, wer die zugrundeliegenden digitalen, unternehmensbezogenen oder akteursübergreifenden institutionellen Arrangements versteht und aktiv gestaltet. Dazu werden ausgereifte, von Forschung und Praxis diskutierte und gemeinsam weiter entwickelte Methoden zum Design digitaler Plattformen, Geschäftsmodelle und Service-Ökosysteme benötigt. Einen gemeinschaftlichen Aufschlag hierzu macht der folgende Artikel. Er entstand aus einem offenen Aufruf in der Wirtschaftsinformatik-Community – über Mailing-Listen und soziale Medien. Im Ergebnis führen sieben Forschergruppen (oder einzelne Forscher und Forscherinnen) ihre aktuellen Erkenntnisse zu diesem Thema zusammen.