The valorization of straw waste as a sustainable and eco-friendly resource in polymer composites is critical for resource recycling and environmental preservation. Therefore, many research works are being carried out regarding the development of wheat straw-based polymer composites to identify the reinforcing potential of these sustainable resources. In this study, three different sizes of wheat straw fibers (60–120 mesh, 35–60 mesh, and 18–35 mesh) were used, and their different ratios (0, 2.5, 5, 10, and 20% by weight) were systematically investigated for the physical and mechanical properties of polypropylene-based sustainable composites. The results indicated that the evaluated composites’ properties are strongly dependent on the quantity and size of the utilized wheat straw. Therefore, a preference selection index was applied to rank the developed sustainable polymer composites to select the best composition. Various properties of the composite materials were considered as criteria for ranking the alternatives, namely tensile strength and modulus, flexural stress at conventional deflection and flexural modulus, impact strength, density, water absorption, material cost, and carbon footprint. The decision-making analysis suggests the alternative with wheat straw content of 20 wt.% (35–60 mesh size) dominating the performance by maximizing the beneficial criteria and minimizing the non-beneficial criteria, making it the most suitable alternative. This study will significantly help formulation designers to deal with the amount and size issues when developing polymeric composites.

This paper presents two examples of usage of fuzzy signatures in the field of mobile robotics. The first shows a complex lateral drift control method base on fuzzy signatures. This method inspects the motion system of the robot as a whole, unlike as simple parts of a complex system. The state space is written down by fuzzy signatures which add up flexibility, adaptability and learning ability to the system. In the second experiment a new communication approach is investigated for intelligent cooperation of autonomous mobile robots. Effective, fast and compact communication is one of the most important cornerstones of a high-end cooperating system. In this paper we propose a fuzzy communication system where the codebooks are built up by fuzzy signatures. We use cooperating autonomous mobile robots to solve some logistic problems. ©2009 IEEE.

The paper deals with the investigation of the critical factor of the supply chain, with regards to time. The literature review can give a background to understand and handle the reasons and consequences of the growing importance of time. It analyses the time- and place-value of products and it evaluates time by showing theoretical functions as well. By using utility functions to represent the value of various delivery-times for the different participants in the supply chain, including the final customers, it proves through the Kano-model, that the choices, behaviour and willingness of payment of time-sensitive and non time-sensitive consumers are different for varying lead times, so for optimization soft computing techniques must be applied. © 2009 IEEE.

In the area of mechanical, aerospace, chemical and civil engineering the solution of partial differential equations (PDEs) has been one of the most important problems of mathematics for a long time. In this field, one of the most exciting areas is the simulation of fluid flow, which involves for example problems of air, sea and land vehicle motion. In engineering applications the temporal evolution of non-ideal, compressible fluids is quite often modeled by the system of Navier-Stokes equations. They are a coupled set of nonlinear hyperbolic partial differential equations and form a relatively simple, yet efficient model of compressible fluid dynamics. Unfortunately the necessity of the coupled multi-layered computational structure with nonlinear, space-variant templates does not make it possible to utilize the huge computing power of the analog Cellular Neural Network Universal Machine (CNN-UM) chips. To improve the performance of our solution emulated digital CNN-UM implemented on IBM Cell Broadband Engine has been used. The goal is to perform the operations with the highest possible parallelism. ©2009 IEEE.

This research introduces a new approach for detecting mobile phone use by drivers, exploiting the capabilities of Kolmogorov-Arnold Networks (KAN) to improve road safety and comply with regulations prohibiting phone use while driving. To address the lack of available data for this specific task, a unique dataset was constructed consisting of images of bus drivers in two scenarios: driving without phone interaction and driving while on a phone call. This dataset provides the basis for the current research. Different KAN-based networks were developed for custom action recognition tailored to the nuanced task of identifying drivers holding phones. The system’s performance was evaluated against convolutional neural network-based solutions, and differences in accuracy and robustness were observed. The aim was to propose an appropriate solution for professional Driver Monitoring Systems (DMS) in research and development and to investigate the efficiency of KAN solutions for this specific sub-task. The implications of this work extend beyond enforcement, providing a foundational technology for automating monitoring and improving safety protocols in the commercial and public transport sectors. In conclusion, this study demonstrates the efficacy of KAN network layers in neural network designs for driver monitoring applications.

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.