The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver’s alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle’s commands.

The feasibility of mixed-cation mixed-halogen perovskites of formula A${}_x$A’${}_{1-x}$PbX${}_y$X’${}_z$X”${}_{3-y-z}$ is analyzed from the perspective of structural stability, opto-electronic properties and possible degradation mechanisms. Using density functional theory (DFT) calculations aided by machine-learning (ML) methods, the structurally stable compositions are further evaluated for the highest absorption and optimal stability. Here, the role of the halogen mixtures is demonstrated in tuning the contrasting trends of optical absorption and stability. Similarly, binary organic cation mixtures are found to significantly influence the degradation, while they have a lesser, but still visible effect on the opto-electronic properties. The combined framework of high-throughput calculations and ML techniques such as the linear regression methods, random forests and artificial neural networks offers the necessary grounds for an efficient exploration of multi-dimensional compositional spaces.

Within the highly complex ecosystem of industry-university collaboration in open innovation, three specific antecedents typically characterize the patterns of their interaction, i.e., motivations, barriers, and channels of knowledge transfer. However, an investigation of the extent to which these antecedents of opening up innovation impact the perceived importance of universities as an external knowledge source to the industry is still missing in the literature. Based on a research framework developed from a review of the literature, a two-stage ordinal regression, and neural network approach was performed to investigate this impact. In the first stage, the hypotheses of the proposed research framework were tested based on an ordinal regression, and those antecedents that significantly impacted the importance perception were revealed. In the second stage, an artificial neural network analysis was carried out to capture the complex relationships among the significant antecedents and the important perception of universities as an external knowledge source to the industry. On the whole, the findings of our study expand the existing open innovation literature and contribute to a more articulate view of the collaboration between industry and university in this field by providing a first perspective on which of the three antecedents has a significant impact on this perception and how such an impact can be predicted.

The reduction in environment pollutant emissions is one of the main challenges regarding ground transportation. Internal combustion engines, used especially in hybrid propulsion systems, may be a solution in the transition to fully electric cars. Therefore, more efficient engines in terms of fuel consumption, emission generation and power density must be developed. This paper presents research regarding the architecture of the combustion chamber of an internal combustion engine with opposed pistons. The aim of this research was to find a combustion chamber architecture that would enable the engine to perform close to the program target of: NOx < 3.5 g/kWh, smoke (FSN) < 1, specific fuel consumption (bsfc) < 198 g/kWh. Three variants of the combustion chamber’s architecture have been studied. After the experimental research, the conclusion was that none of them fully reached the target; however, significant improvements have been achieved compared with the starting point. As a result, further research needs to be carried out in order to reach and even exceed the target.