The paper proposes a multi-layer supervisory architecture for integrated control systems in road vehicles. The role of the supervisor is to coordinate active control components and provide priority among them. The supervisor has information about the current operational mode of the vehicle and it is able to make decisions about the necessary interventions into the vehicle components and guarantee the reconfigurable operation of the vehicle. The decisions of the supervisor are propagated to the lower layers through predefined interfaces encoded as suitable scheduling signals. The contribution of the paper is the application of the LPV methodology in a design case study in which an integrated control of four wheel independently-actuated electric vehicle with active steering system is developed.

Calving interval (CI) data (N = 37,263) from 17,319 cows born 2008–2018 in six herds were assessed. The data were made available by the National Association of Hungarian Holstein Friesian Breeders in Hungary. The effects of some genetic and environmental factors, population genetic parameters, breeding value (BV) of sires, and phenotypic and genetic trends of the CI were estimated. The GLM method was used for studying different effects on the CI. BLUP animal model was used for heritability (h²) and BV estimation. Linear regression analyses were applied for the trend calculation. The mean of the CI was 412.2 ± 2.0 days. The h² of the CI proved to be low (0.07 ± 0.01 and 0.08 ± 0.01). There were relatively high differences among the sires in the estimated BV. Based on the phenotypic trend calculation, the CI of cows showed decreasing direction by an average of 1.80 days per year (R² = 0.94; p < 0.01). In the case of genetic trend calculation, the average BV of sires in the CI has decreased −4.94 and −0.31 days per year (R² = 0.91 and 0.41; p < 0.01).

Introduction Landslides and sinkholes damage social, economic, and natural infrastructure. These processes have direct and indirect impacts on important infrastructure, including residential areas, and influence land use change and migration from rural to urban areas. Sinkholes and landslides occur when parts of a soil collapse mainly in more gentle or steeper slopes, which are often triggered by intensive rainfall. One of the main goals in sustainable land management is the identification and control of natural disasters, which on the one hand leads to the quantitative and qualitative improvement of production in the long term, and on the other hand, maintains the quality of the soil and prevents soil degradation. In order to manage better and more stable, it seems necessary to know how to change and identify different forms of erosion such as sinkholes and landslides. Sinkholes and landslides occur when parts of a soil collapse mainly in more gentle or steeper slopes, which are often triggered by intensive rainfall. Materials and methods Recent advances in acquiring images from unmanned aerial vehicles (UAV) (UAV) and deep learning (DL) methods inherited from computer vision have made it feasible to propose semi-automated soil landform detection methodologies for large areas at an unprecedented spatial resolution. In this study, we evaluate the potential of two cutting-edge DL deep learning segmentation models, the vanilla U-Net model, and the Attention Deep Supervision Multi-Scale U-Net model, applied to UAV-derived products, to map landslides and sinkholes in a semi-arid environment, the “Golestan Province” (north-east Iran). Results and discussion Landslides: The performance of the U-Net model shows that it has fewer false positives, but at the same time, it has missed many landslide cells. Meanwhile, the ADSMS U-Net model has performed better in detecting landslide cells, but it attributed many cases to incorrect predictions (which is explained by the low accuracy score). The best F1 score achieved for the ADSMS U-Net model is 0.68. Sinkholes: For all band combinations, the performances of ADSMS U-Net are better than those of the traditional U-Net model. The best overall scores by ADSMS U-Net were obtained when trained on the ALL data. Regarding the effectiveness of the various combinations evaluated in this study, we can observe the contradictory behaviors of the models. The traditional U-Net achieves the best performance using the RGB optical combination, while the ADSMS U-Net can leverage topographic derivative information and optical data, showing the best results with the ALL combination. Moreover, it is evident that the DSHC data alone provides the worst results for both models. In overall, the results show that the ability of ADSMS U-Net to predict landslides is closer to the ground reality compared to U-Net. This model identifies most of the landslides in the test sections. Also, for all combinations of sinkhole bands, ADSMS U-Net performs better than the U-Net model. The best overall scores were obtained by ADSMS U-Net when trained on ALL data. Conclusions Since this kind of soil erosion is the main origin of some major soil erosion including gully initiation and extension, applying new technology namely, UAV and deep learning is highly important and recommended. Our framework can successfully map landslides in a challenging environment (with an F1-score of 69 %), and topographical derivates from UAV-derived DSM decrease the capacity of mapping sinkholes and landslides of the models calibrated with optical data. Future research could explore the use of such an approach to map landslides and sinkholes over time to assess time-based changes in the formation and spread of natural hazards.

Startups, understood as new forms of innovative and fast-growth ventures, are emerging in traditional industries, creating intense competition and displacing former leaders. Our study focuses on location theory embedded in institutional and resource context and its application to startups in the Visegrad countries. We know a lot about the location choices made by small and medium-sized enterprises (SMEs). However, research on the location preferences of startups is limited, especially within the transition economies of Central and Eastern Europe. We investigated the differences in location decisions between startups and SMEs and those between startups located in metropolitan areas and rural areas. A study on the location decisions of startups was conducted in 2021 using mixed methods. The research showed that local factors strongly influence startups. It may seem obvious that large cities provide startups with access to resources, markets and support through the local innovation ecosystem. However, our analysis identified three significant differences between startups and traditional SMEs regarding location choice. For startups, the availability of skilled workforce and an R&D center/research university is more difficult. In contrast, local (family) ties and rootedness are more important for rural startups than metropolitan ones. This study provides new evidence on how spatial externalities affect innovative startups in the Visegrad countries and identifies factors that influence the location of startups in urban and rural areas, with a particular focus on Hungarian startups. For the latter, the study shows that state aid to startups has an ambiguous effect on the shape of the ecosystem, producing contradictory effects on the development of startups in the region. Given the methodological limitations described in our paper, further research is advisable to deepen the study of localization theory in the context of startups in the CEE region, especially in the V4 counties.

The literature on blockchain-enabled use cases has grown exponentially over recent years. Yet, studies are missing that apply bibliometrics and visualization techniques to unravel the dynamics and current discussions pertaining to the nexus of blockchain technology (BCT) and the healthcare field. To close this knowledge gap, we examine the knowledge base and research hotspots of BCT research in the field of healthcare. We carry out a series of bibliometric analyses on the extant literature, including the scholarly production, developmental pattern of the annual total number of authors, and identification of productive academic institutions, countries, and leading authors. Additionally, we conduct a keyword co-occurrence analysis and identify the major research hotspots and trends for the future. The findings of this research are valuable for scholars and practitioners who seek to better understand the development status, dynamics, and trends pertaining to BCT in healthcare.

The aim of the Traveling Salesman Problem (TSP) is to find the cheapest way of visiting all elements in a given set of cities and returning to the starting point. In solutions presented in the literature costs of travel between nodes (cities) are based on Euclidean distances, the problem is symmetric and the costs are constant. In this paper a novel construction and formulation of the TSP is presented in which the requirements and features of practical application in road transportation and supply chains are taken into consideration. Computational results are presented as well.

Automated Driving functions have widely spread in last years, not only in research, but also in the automotive industry. Many car manufacturers and their suppliers have funded different software systems to fulfill the driving requirements. By the growing number of innovations, the rapidly changing technology and newer players in the automotive industry requires academy and industry to collaborate on their automated software developments. To do so, a common research and development framework is needed which inherently provides smooth transition of new solutions from one world to the other. Even though there are many open source research framework for rapid prototyping of automated driving functions, these solutions are often concentrated on low speed maneuvering, especially for the higher automation levels, such as robo-taxis. However, major automotive companies require developments of up to SAE (Society of Automotive Engineers) level 2 and 3. Therefore, a new prototypical, open source software architecture¹ is proposed, that uses well-known tools and technology as basis, such as Robot Operating System or Autoware Universe, but also being closely in compliance with industrial architecture aspects. It is proven that the proposed architecture, along with its abstraction layers and interfaces provide modularity, and interchangeability of basic components, while the hardware and vehicle dependencies are well separated from the application software layer. Via few exemplary functions the usability of the system is demonstrated, and fellow researchers are highly encouraged to advise on the generalization of the system.¹https://github.com/jkk-research/CooperativeResearchPlatform/tree/release

The semantic synthesis of intelligent space, emotional-behavioral modeling and cognitive infocommunications is a promising approach in the multidisciplinary field applied for ambient assisted living. The ambient assisted living application can be regarded as a complex cooperation and communication of different kinds of intelligent entities in a well-limited, semi-structured environment. The intelligent space aspect deals with the possible cooperation of intelligent entities as matching between needs and capabilities. The emotional-behavioral modeling handles the internal states and attitudes of the entities. The cognitive info-communications aspect deals with communication situations and processes between the entities. The semantic information framework is intended to provide a synthesis of the different fields in order to unite the represented knowledge in the form of information transfer between the field scopes. © 2013 IEEE.

This study examines the complex interaction between social multi-dimensionality and green energy financial literacy in Mexico. Drawing on a mixed-methods approach with quantitative analysis of Mexican national survey data and qualitative interviews with stakeholders, this research examines how socio-economic status, place, education levels, and indigenous identity intersect to form barriers to green energy financial literacy. The findings reveal wide disparities in the availability of information on green energy finance, and indigenous, rural, and poor communities report significantly lower levels of financial literacy for renewable energy investment. The research adds to the pool of knowledge on how social disparities perpetuate environmental and economic disparities in emerging economies in their developmental path towards sustainable energy infrastructure.