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.

In the era of COP28, where most of the developed and developing economies concentrate more on the development of environmentally friendly energy resources to tackle the issue of climate change. Nevertheless, the literature lacks appropriate evidence regarding the influences of green energy and other resources on food security. This study analyses the influences of land use, green energy, and water resources on food accessibility in emerging economies, while also considering the important roles of natural resources, research and development (R&D) expenditure, and economic growth during 1980–2020. Due to non-linear data dispersion, the novel moments quantile regression is employed. Results assert that land use has a positive significant influence on food accessibility in the presence of water resources and a weaker negative impact in the presence of natural resources. Natural and water resources are detrimental to food accessibility in the Emerging Seven (E7) countries. Furthermore, R&D expenditure and green energy positively (negatively), while economic growth negatively (positively) impacted food accessibility in the presence of natural resources (water resources). The results are robust and validate causal inferences that help develop appropriate policies for emerging economies concerning food accessibility or security. In this rapidly evolving era, most empirical studies consider environmental quality. Conversely, this study contributes to the literature by examining the factors influencing food accessibility, as this issue is of considerable importance because of the rapidly growing global population.

In this study, the concept of symmetry is employed to implement an intelligent fuzzy traffic signal control system for complex intersections. This approach suggests that the implementation of reduced fuzzy rules through the reduction method, without compromising the performance of the original fuzzy rule base, constitutes a symmetrical approach. In recent decades, urban and city traffic congestion has become a significant issue because of the time lost as a result of heavy traffic, which negatively affects economic productivity and efficiency and leads to energy loss, and also because of the heavy environmental pollution effect. In addition, traffic congestion prevents an immediate response by the ambulance, police, and fire brigades to urgent events. To mitigate these problems, a three-stage intelligent and flexible fuzzy traffic control system for complex intersections, using a novel hybrid reduction approach was proposed. The three-stage fuzzy traffic control system performs four primary functions. The first stage prioritizes emergency car(s) and identifies the degree of urgency of the traffic conditions in the red-light phase. The second stage guarantees a fair distribution of green-light durations even for periods of extremely unbalanced traffic with long vehicle queues in certain directions and, especially, when heavy traffic is loaded for an extended period in one direction and the short vehicle queues in the conflicting directions require passing in a reasonable time. The third stage adjusts the green-light time to the traffic conditions, to the appearance of one or more emergency car(s), and to the overall waiting times of the other vehicles by using a fuzzy inference engine. The original complete fuzzy rule base set up by listing all possible input combinations was reduced using a novel hybrid reduction algorithm for fuzzy rule bases, which resulted in a significant reduction of the original base, namely, by 72.1%. The proposed novel approach, including the model and the hybrid reduction algorithm, were implemented and simulated using Python 3.9 and SUMO (version 1.14.1). Subsequently, the obtained fuzzy rule system was compared in terms of running time and efficiency with a traffic control system using the original fuzzy rules. The results showed that the reduced fuzzy rule base had better results in terms of the average waiting time, calculated fuel consumption, and CO2 emission. Furthermore, the fuzzy traffic control system with reduced fuzzy rules performed better as it required less execution time and thus lower computational costs. Summarizing the above results, it may be stated that this new approach to intersection traffic light control is a practical solution for managing complex traffic conditions at lower computational costs.

This research introduces an innovative approach for End-to-End steering angle prediction and its control in electric power steering (EPS) systems. The methodology integrates transfer learning-based computer vision techniques for prediction and control with fuzzy signatures-enhanced fuzzy systems. Fuzzy signatures are unique multidimensional data structures that represent data symbolically. This enhancement enables the fuzzy systems to effectively manage the inherent imprecision and uncertainty in various driving scenarios. The ultimate goal of this work is to assess the efficiency and performance of this combined approach by highlighting the pivotal role of steering angle prediction and control in the field of autonomous driving systems. Specifically, within EPS systems, the control of the motor directly influences the vehicle’s path and maneuverability. A significant breakthrough of this study is the successful application of transfer learning-based computer vision techniques to extract respective visual data without the need for large datasets. This represents an advancement in reducing the extensive data collection and computational load typically required. The findings of this research reveal the potential of this approach within EPS systems, with an MSE score of 0.0386 against 0.0476, by outperforming the existing NVIDIA model. This result provides a 22.63% better Mean Squared Error (MSE) score than NVIDIA’s model. The proposed model also showed better performance compared with all other three references found in the literature. Furthermore, we identify potential areas for refinement, such as decreasing model loss and simplifying the complex decision model of fuzzy systems, which can represent the symmetry and asymmetry of human decision-making systems. This study, therefore, contributes significantly to the ongoing evolution of autonomous driving systems.

Soil organic matter (SOM) concentration and composition are essential properties that affect most functions and ecosystem services. The relationship between soil and environmental covariates regarding SOM concentration and composition in various pools is not completely understood. This study aimed to identify the most influential drivers of SOM stabilization, focusing on arable lands in Hungary. Hungary is an ideal area for investigating SOM variability because it is at the meeting point of the three main climate effects that trigger a wide range of soil, land use, and topographical conditions. Overall, 87 soil samples were taken from the topsoil (2–20 cm) and fractionated (<20 µm) to separate the mineral phase-associated organic carbon (MAOC) and bulk pools. MAOC concentration varied on a wide range (0.5–14.1 %) and was the function of bulk SOM aromaticity and slope steepness, rather than land use, climatic conditions, or soil properties, indicating that MAOC is also affected by decomposition if the bulk OM is less available for the microbiome. Land use, especially in forest topsoils, reflects the elemental composition of the OM, focusing on the variations of residue composition. In contrast, aromaticity is rather related to soil and climate properties, suggesting increased relevance of transformation processes. As a consequence, SOM composition varies on a wide range in the topsoil, however, the lack of a definite trend at the county level suggests the complexity of the system and highlights the role of local circumstances.

The cross-level and twist irregularities are the most dangerous irregularity types that could cause wheel unloading with the risk of derailments and additional maintenance expenses. However, the mechanism of the irregularities initiation and development is unclear. The motivation of the present study was the previous experimental studies on the application of wide sleepers in the ballasted track. The long-term track geometry measurements with wide sleepers show an enormous reduction of the vertical longitudinal irregularities compared to the conventional track. However, wide sleepers had higher twist and cross-section level irregularities. The present paper aims to explain the phenomenon by discrete element method (DEM) modeling the development process of sleeper inhomogeneous support at cross-level depending on the sleeper form. The DEM simulations show that the maximal settlement intensity is up to 3.5 times lower for a wide sleeper in comparison with the conventional one. Nevertheless, the cross-level differential settlements are almost the same for both sleepers. The particle loading distribution after all loading cycles is concentrated on the smaller area, up to the half sleeper length, with fully unloaded zones under sleeper ends. Ballast flow limitation under the central part of the sleeper could improve the resilience of wide sleepers to the development of cross-level irregularities. The mechanism of initiation of the cross-level irregularity is proposed, which assumes the loss of sleeper support under sleeper ends. The further growth of inhomogeneous settlements along the sleeper is assumed as a result of the interaction of two processes: ballast flow due to dynamic impact during void closing and on the other side high pressure due to the concentration of the pressure under the middle part of the sleeper. The DEM simulation results support the assumption of the mechanism and agree with the experimental studies.

Purpose: To assess the prevalence, types, sociodemographic factors, and reported dangerous activities of sleep-related behaviors likely representing NREM parasomnia episodes, as well as their association with adverse childhood experiences in Hungary. Methods: Cross-sectional survey of 1000 adults (aged ≥18 years) representing the Hungarian population, using a non-probability quota sampling with a random walk method and a structured face-to-face interview. A multi-criterion weighting procedure was applied to correct bias along the main sociodemographic variables to the data available. Binary logistic regression estimated the odds of NREM parasomnia-related behaviors associated with sociodemographic factors and adverse childhood experiences. Results: The prevalence of NREM parasomnia-related behaviors was 2.7 %, and self-reported sleep-eating was 0.1 % of the population (4.6 % of parasomnia-like activities). For middle-aged adults, the odds of sleep ambulation were significantly lower than for younger adults (OR 0.3; P = 0.03). A participant's family occurrence of reported parasomnia-like activity increased their odds of having it by more than 7 times (OR 7.1; P < 0.001). Nine participants out of those 27 people reporting NREM parasomnia-related behavior episodes, reported childhood adverse experiences, increasing the odds of parasomnia-related behavior by more than six times (OR 6.2; P < 0.001) compared to those not reporting it. Conclusion: This is the first population survey in Hungary on adult sleep-related behaviors likely representing NREM parasomnia episodes and the potential association with childhood traumatic events preceding them. The related dangerous behaviors call for safety measures and prevention. The significant association between adverse childhood events and NREM parasomnia-related behaviors needs further analysis.

The most common health-related apps are lifestyle apps, i.e., fitness, nutrition, diet, and meditation apps, which account for half of all m-health apps on the market. Mobile app-based interventions have been shown to be effective in improving diet-related health outcomes. The aim of this study is to map the usage patterns of lifestyle apps (fitness, diet, and relaxation apps) and identify the role of each factor in the usability of MAUQ (m-Health App Usability Questionnaire) factor - ease of use, interface satisfaction, and usefulness - in overall satisfaction. Data were collected through an online survey in Hungary with 348 users of various lifestyle applications, i.e., fitness (30.2%), nutrition (31.3%), and mindfulness (38.5%) apps. Respondents showed a preference for free apps over paid ones and predominantly used iOS operating systems. The partial least squares structural equation modelling (PLS-SEM) method was used to identify the role of usability dimensions in overall satisfaction. The satisfaction of lifestyle app users is positively influenced by 'Ease of Use' and 'Interface and Satisfaction'. However, effectiveness (positive physical and mental health outcomes) negatively influences satisfaction. Research can be particularly useful for app developers, as usability and design (features) play a particularly important role in satisfaction, so these are primary considerations in development.

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.