Our university is determined to research and educate self-driving and autonomous technology. These two field require different necessities and attitude. In his paper we would like to summarize the migration from a road-legal vehicle to a self-developed, one-seated vehicle. We will describe the challenges of the migration process and of course how to overcome these challenges. The current paper also proposes recommendations and use-cases regarding self-driving vehicle sensory system.

The Traveling Salesman Problem (TSP) is one of the most often studied NP-hard graph search problems. There have been numerous publications in the literature that applied various approaches to find the optimum or semi optimum solution. Although the problem is computationally intractable, but the Time Dependent Traveling Salesman Problem (TD TSP) is one of the most realistic extensions of the original TSP problem. In the TD TSP, the costs of edges between nodes vary, namely, they are assigned higher costs if they crossed a predefined oblong shaped area (to represent the jam region in the city center). Realizing that the jam regions and the rush hours costs on a tour are uncertain and can never be accurately represented by concrete numbers, we introduced the novel 3FTD TSP (Triple Fuzzy Time Dependent Traveling Salesman Problem); a fully fuzzified model of the original TD TSP. The 3FTD TSP utilizes fuzzy values for determining the costs between any two nodes within the traffic jam regions and during the rush hours periods more precisely. In this paper, we extend the 3FTD TSP further and apply it on the biggest universal instances in the literature in pursuit of testing the generality and applicability of the 3FTD TSP on real-life scenarios. To support the claim of the model’s efficiency, we propose the application of the DBMEA (Discrete Bacterial Memetic Evolutionary Algorithm), as a meta-heuristic and the classic GA (Genetic Algorithm) enabling the reader to compare the accuracy and the speed of (quasi-) optimum solutions convergence.

Background: Sepsis-related cardiomyopathy is mainly induced by uncontrolled inflammation and mitochondrial oxidative damage triggered by endotoxins, especially those of Gram-negative bacteria. Lipopolysaccharide (LPS), an endotoxin, activates the Toll-like receptor 4/nuclear factor kappa B (TLR4/NF-κB) signaling pathway and the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome, which further activates caspase-1 to process the pro-inflammatory cytokines interleukin-1β (IL-1β) and interleukin-18 (IL-18). Chronic activation of this signaling pathway is responsible for mitochondrial damage and loss of membrane potential. Baicalein, a flavonoid derived from the herbal plant Scutellaria baicalensis is well recognized for its anti-inflammatory and antioxidant properties, although its full modulation of the NF-κB/NLRP3 mitochondrial signaling pathway is not thoroughly understood. Objectives: In this study, we assessed whether baicalein has a protective effect on H9c2 cardiomyoblasts against inflammatory and mitochondrial injury induced by LPS through regulation of inflammatory pathways involving NF-κB, the inflammasome, caspase-1 activity, cytokine expression, and oxidation. Methods: H9c2 cells were pre-exposed to different baicalein concentrations (5 - 20 µM) and, after 1 hour, exposed to 1 µg/mL of LPS. Analysis included cell viability measurements by MTT assay, lactate dehydrogenase (LDH) release, and microscopic evaluation by phase-contrast microscopy. Expression levels of premature Nlrp3, Il1B, Il18, and Casp1 (0 - 4 h) were analyzed by real-time polymerase chain reaction (PCR). Protein levels of inflammasome and NF-κB pathway proteins were evaluated by immunoblot analysis. Activity of caspase-1 and cytokine secretion of IL-1β and IL-18 were evaluated by colorimetry and enzyme-linked immunosorbent assay (ELISA), respectively. Mitochondrial membrane potential (ΔΨm) was evaluated by staining with JC1 dye, and reactive oxygen species (ROS) levels were evaluated by fluorescence with DCFH2DA; N-acetylcysteine (NAC) was used as an antioxidant positive control. Results: LPS strongly elevated the transcription of Nlrp3 and Il1b at 2 hours, in addition to enhancing protein expression of NLRP3, activity of caspase-1, and secretion of IL-1β and IL-18. Underlying these pro-inflammatory responses were mitochondrial depolarization, augmented ROS production, diminished cell survival, and cytotoxicity. Prior administration of baicalein mitigated NF-κB activation, diminished priming of the inflammasome response at both transcriptional and protein levels, and decreased caspase-1 activity and secretion of the cytokines. Moreover, baicalein maintained mitochondrial membrane potential and diminished ROS within the cells, comparable to NAC, and improved cellular viability and morphology Conclusions: Baicalein confers substantial protection against LPS-induced inflammatory and oxidative damage in H9c2 cardiomyoblasts by concurrently suppressing NF-κB activation, mitigating NLRP3 inflammasome signaling, curbing caspase-1–driven cytokine maturation, and stabilizing mitochondrial function through reduction of ROS. These findings identify baicalein as a promising candidate for targeting the ROS-inflammasome axis in sepsis-associated cardiac dysfunction and related inflammatory cardiac disorders.

This study presents a fuzzy-Apriori model that analyses student background data, along with end-of-lesson student-generated questions, to identify interpretable rules. After linguistic and semantic preprocessing, questions are represented in a fuzzy form and combined with background and performance variables to generate association rules, including support, confidence, and lift. The dataset includes 202 students, parent reports from 174 families, 5832 student-generated questions, and 510 teacher-generated questions collected in regular lessons in grades 7–8. The model also incorporates a topic-level dynamic updating step that refreshes the rule set over time. The findings indicate descriptive associations between background characteristics, question complexity and alignment, and classroom performance. It is essential to note that this phase explores possibilities rather than providing a validated instructional method. Question coding inevitably involves subjective elements, and while we conducted the study in real classroom settings, we did not perform causal analyses at this stage. The next step will be developing reliability metrics through longitudinal studies across multiple classroom environments. Future work will test whether using these patterns can inform instructional adjustments and support student learning.

This paper addresses the impact of spatial allocation on the flexibility requirements of wind farms in the context of the mid-term development of wind energy exploitation in Hungary. As a starting point for evaluating flexibility requirements, a wind power simulation model is adopted that converts hourly wind speed time series derived from climate reanalysis into hourly aggregate power output using theoretical power curves. This simulation model allows for evaluating various flexibility metrics for both existing and hypothetical wind farms. As a second step, a Genetic Algorithm (GA) is integrated with the simulation environment to find an optimal subset of hypothetical wind farms with respect to flexibility requirements. The application of GA reveals insights into how spatially optimizing wind farm placement can significantly reduce flexibility requirements. This finding can have practical implications for policy-makers and planners in the renewable energy sector, especially given the evolving regulatory landscape and increasing focus on grid stability.

The present study evaluated the feasibility of the fortification of dark chocolate with a freeze-dried lemon balm extract rich in rosmarinic acid (RA). Given the food matrix used, it was necessary to develop an analytical and extraction method for RA determination in fortified chocolate. A simple and efficient reversed-phase high-performance liquid chromatography method (RP-HPLC) has been developed for the estimation of RA in dark chocolate. The validity of the method was demonstrated by evaluating the system suitability, linearity, precision, accuracy, the limit of detection, and limit of quantification. The extraction of RA from chocolate was optimized using maceration with shaking (MACS) and microwave-assisted extraction (MAE). The experimental conditions, namely solvent type and extraction time, were optimized using the one-factor-at-a-time methodology. Under the optimized conditions, the proposed method showed good recovery within 95% and acceptable reproducibility with intra- and inter-day relative standard deviations of <2.3%. The method was linear from 5 to 800 μg/mL with a determination coefficient of 1.000. Moreover, the functional chocolate showed a homogeneous active ingredient distribution and retained its RA amount since no significant difference was observed during storage. Overall, the present method was successfully applied to determine RA content in fortified chocolate.

The criminal legislation regarding zoophilia and the legal status of animals were examined in 15 European countries. With regard to zoophilia, answers to the following questions were sought: are sexual acts performed with animals and the possession and distribution of animal pornography criminally punishable? Several aspects of the legal status of animals were examined including: (1) is the protection of individual animals included in the constitution, (2) do animals have a special status beyond mere objects, (3) can we find specific legislative provisions that explicitly state that animals are not simply things, and (4) does the legal system also take the “dignity” of animals into consideration. The assessment of zoophilia and the legal status of animals resulted in two country rankings, which the authors compared with each other. The correlation was not significant (p = 0.3147). At the same time, countries with differentiated criminal legislation for zoophilia were also 3.62 times more likely to rank higher in terms of the legal status of animals. The Swiss regulations are exceptional in both respects, while at the other end of the list, Italy does not have specific legislative provisions for either aspect.

Ensuring task safety in Human-Robot Interaction (HRI) environments is a critical requirement for reliable and trustworthy robotic systems. AI can be used effectively to estimate robot task safety. However, existing systems suffer from limited data availability and class imbalance, resulting in inaccurate detection of unsafe events. To address these issues, a hybrid speech data augmentation approach is proposed, which combines acoustic and linguistic approaches to train the ML models effectively. The experimentation involves implementing the hybrid augmentation approach, with acoustic transformations for features such as audio level and linguistic transformations for speech data. Results indicate that various Machine Learning models show enhanced performance, achieving up to 0.97 accuracy with the hybrid approach, while the other augmentation approaches achieve lower results, with accuracy ranging from 0.66 to 0.92. In addition, Explainable AI (XAI) strategies are employed to highlight key contributions of significant characteristics such as speech data, audio level, and robot position.

The study examines the relationship between financial diligence, risk tolerance, and financial situation assessment, as well as their correlations with various socio-demographic factors. The authors used the 2022 Hungarian dataset from the OECD’s (Organisation for Economic Co-operation and Development) recurring international financial literacy survey for their analysis. The results indicate that respondents generally monitor their finances carefully, tend to avoid risks, and report low levels of satisfaction. A moderately strong positive relationship exists between the dimensions of financial behavior. This suggests that both diligence and appropriate risk management are crucial for financial awareness and contribute to financial satisfaction. Through cluster analysis, ten well-distinguished groups were identified differing in financial behavior and attitudes. Differences between the clusters highlight that socio-demographic factors – such as age, income, type of residence, and employment status – fundamentally influence individuals' financial decision-making.