In this paper we present a metaheuristic method, called DBMEA. It combines the bacterial evolutionary algorithm with local search techniques. Based on our test results it can be used for solving efficiently more discrete optimization problems. The algorithm was tested on Traveling Salesman Problem and Traveling Repairman Problem (TRP) benchmark instances found in the literature. In the case of TSP the DBMEA algorithm produced optimal or near-optimal solutions for all tested instances. Although the most efficient TSP solver method, the Helsgaun's Lin-Kernighan heuristic was faster than DBMEA, but in the case of DBMEA the runtime was more predictable than it the case of other methods. In the case of TRP the results are competitive in terms of accuracy and runtimes with the state-of-the art methods. Except two instances our algorithm found the best-known solutions, and for the biggest tested instance it found new best solution. The runtime was on average 30% faster than the most efficient heuristic in the literature.

Multilateral, subregional partnerships are one of the defining features of contemporary European politics. The 2004 enlargement of the European Union introduced a new partnership entity to the mix-the Visegrád Group of states, comprising the Czech Republic, Hungary, Poland and Slovakia-which offers its members a useful model to discuss and represent common interests in a collective manner within the regional and international political landscape. The article provides a reflection on the evolution of the Visegrád Group of states since 2004. It examines four key policy areas (institutional candidacy, energy policy, eastern neighbourhood and defence policy) where the group has either learned some difficult lessons or is seeking to apply those lessons. The article suggests that the Visegrád Group's greatest strength lies in its flexibility to become more responsive to the challenges it faces and it is likely therefore to remain appealing to its member states and to be a permanent feature within the wider cohort of European Union subregional partnerships. © 2013 University of Glasgow.

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.

Social sustainability, the third pillar of sustainability alongside ecological and economic dimensions, focuses on maintaining the well-being and viability of communities, as a healthy functioning society is a prerequisite for sustainable operation. Trust, a common measure of social sustainability, is a fundamental pillar of a sustainable, resilient, inclusive, transparent and accountable society. This research aims to analyse how trust as a measure of social sustainability interacts with different levels of governance and institutional performance in the Visegrád Four countries. The study found that although there is a significant correlation between various factors of institutional trust at the national level, there is no meaningful relationship between general and institutional trust across the examined countries (Czech Republic, Hungary, Slovakia, and Poland). The analysis found that institutional trust is influenced more by endogenous factors, such as perceived governance effectiveness and citizen satisfaction, than by general trust. However, no strong correlation could be shown between Word Governance Indicator and institutional trust. The findings show that trust does not progressively decline towards higher levels of institutions; instead, proximity and citizen engagement boost trust at the local level, while different mechanisms likely drive trust at higher government levels. Additionally, the research highlights that in post-communist societies, strong traditions of particular trust limit general trust in institutions, emphasizing the complexity of trust dynamics within different governance levels.

This paper reviews the works related to the effect of soil compaction on cereal yield and focuses on research of field experiments. The reasons for compaction formation are usually a combination of several types of interactions. Therefore one of the most researched topics all over the world is the changes in the soil's physical and chemical properties to achieve sustainable cereal production conditions. Whether we are talking about soil bulk density, physical soil properties, water conductivity or electrical conductivity, or based on the results of measurements of on-line or point of soil sampling resistance testing, the fact is more and more information is at our disposal to find answers to the challenges. Thanks to precision plant production technologies (PA) these challenges can be overcome in a much more efficient way than earlier as instruments are available (geospatial technologies such as GIS, remote sensing, GPS with integrated sensors and steering systems; plant physiological models, such Decision Support System for Agrotechnology Transfer (DSSAT), which includes models for cereals etc.). The tests were carried out first of all on alteration clay and sand content in loam, sandy loam and silt loam soils. In the study we examined especially the change in natural soil compaction conditions and its effect on cereal yields. Both the literature and our own investigations have shown that the soil moisture content changes have the opposite effect in natural compaction in clay and sand content related to cereal yield. These skills would contribute to the spreading of environmental, sustainable fertilizing devoid of nitrate leaching planning and cereal yield prediction within the framework of the PA to eliminate seasonal effects.

Weeds have the potential to cause significant damage to agricultural fields, so the development of weed detection and automatic weed control in these areas is very important. Weed detection based on RGB images allows more efficient management of crop fields, reducing production costs and increasing yields. Conventional weed control methods can often be time-consuming and costly. It can also cause environmental damage through overuse of chemicals. Automated weed detection and control technologies enable precision agriculture, where weeds are accurately identified and targeted, minimizing chemical use and environmental impact. Overall, weed detection and automated weed control represent a significant step forward in agriculture, helping farmers to reduce production costs, increase crop safety, and develop more sustainable agricultural practices. Thanks to technological advances, we can expect more efficient and environmentally friendly solutions for weed control in the future. Developing weed detection and automated control technologies is crucial for enhancing agricultural efficiency. Employing RGB images for weed identification not only lowers production costs but also mitigates environmental damage caused by excessive chemical use. This study explores automated weed detection systems, emphasizing their role in precision agriculture, which ensures minimal chemical use while maximizing crop safety and sustainability.

Seismic site response is inevitably influenced by natural variability of soil properties and anticipated earthquake intensity. This study presents the influence of variability in shear wave velocity (Vs) and amplitude of input rock motion on seismic site response analysis. Monte Carlo simulations were employed to randomize the Vs profile for different scenarios. A series of 1-D equivalent linear (EQL) seismic site response analyses were conducted by combining the randomized Vs profile with different levels of rock motion intensities. The results of the analyses are presented in terms of surface spectral acceleration, amplification factors (AFs), and peak ground acceleration (PGA). The mean and standard deviation of these parameters are thoroughly discussed for a wide range of randomized Vs profile, number of Vs randomizations, and intensities of input rock motions. The results demonstrate that both the median PGA and its standard deviations across different number of Vs profile realization exhibit a slight variation. As few as twenty Vs profile realizations are sufficient to compute reliable response parameters. Both rock motion intensity and standard deviation of Vs variability cause significant variation in computed surface parameters. However, the variability in the number of records used to conduct site response has no significant impact on ground response if the records closely match the target spectrum. Incorporating the multiple sources of variabilities can reduce uncertainty when conducting ground response simulations.

The present study focuses on the spatial aspect of the liturgy, the raison d'être of church building. The topicality of this approach is explained either by the manner of international communication about sacred architecture, or by the work of German architect Rudolf Schwarz (1897-1961), who is cited anew, very often due to his unification of architectural praxis, scientific activity and a life of a devoted man deservedly still influential in the field. A church can be understood by its operative function. Throughout the most important liturgical reform movements of the 20th century, one can get an insight into the spatial concepts of the designers, which, consciously or not, reflect the achievements of these movements even today. In the contemporary praxis of Latin Rite liturgy, the divorce between the once close-knit aspects of the worship are palpable, thanks to these reforms on the one hand, and their late interpretations on the other. Inspired by autonomous readings, the sacrificial act of the Eucharist (sacrificium) and the symbol of the communal feast (cena) were associated with correspondent models of space. This problem shares root with the frequent contraposition of personal devotion and the communio: the former prevails in oriented spaces, while the latter appears in the central. Spatial concepts capable of respecting both religious devotion and the needs of the community in their primordial unity and theological profoundness, seldom can be found today. Following the terminology of Albert Gerhards and Walter Zahner, these are often referred to as communio-spaces (Communio-Räume). Albeit the proposition of this space type is clearly theoretical, it looks back to important antecedents from the 20th century, and defines a multiplicity of new directions. The story of communio-space spans from Rudolf Schwarz to the contemporary church architecture of Central and Western Europe as well as North America, and still influences the newest designs of liturgical spaces. In Hungary, this recent issue of spatial arrangement is not a question of a substantial debate up to now, yet even this limited number of experiments necessitate the idea and applied examples of communio-space to acquire more attention here as well.

Introduction: As the number of people with visual impairments (that is, those who are blind or have low vision) is continuously increasing, rehabilitation and engineering researchers have identified the need to design sensory-substitution devices that would offer assistance and guidance to these people for performing navigational tasks. Auditory and haptic cues have been shown to be an effective approach towards creating a rich spatial representation of the environment, so they are considered for inclusion in the development of assistive tools that would enable people with visual impairments to acquire knowledge of the surrounding space in a way close to the visually based perception of sighted individuals. However, achieving efficiency through a sensory substitution device requires extensive training for visually impaired users to learn how to process the artificial auditory cues and convert them into spatial information. Methods: Considering all the potential advantages game-based learning can provide, we propose a new method for training sound localization and virtual navigational skills of visually impaired people in a 3D audio game with hierarchical levels of difficulty. The training procedure is focused on a multimodal (auditory and haptic) learning approach in which the subjects have been asked to listen to 3D sounds while simultaneously perceiving a series of vibrations on a haptic headband that corresponds to the direction of the sound source in space. Results: The results we obtained in a sound-localization experiment with 10 visually impaired people showed that the proposed training strategy resulted in significant improvements in auditory performance and navigation skills of the subjects, thus ensuring behavioral gains in the spatial perception of the environment.