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Bridges in Ukraine are one of the most important components of the infrastructure, requiring attention from government agencies and constant funding. The object of the study was the methodology for quantifying the condition of bridge components. The Artificial Neural Network-based (ANN) tool was developed to quantify the technical condition of bridge components. The literature analysis showed that in most cases the datasets were obtained during the inspection of bridges to solve the problems of assessing the current technical condition. The lack of such a database prompted the creation of a dataset on the basis of the Classification Tables of the Operating Conditions of the Bridge Components (CT). Based on CTs, five datasets were formed to assess the condition of the bridge components: bridge span, bridge deck, pier caps beam, piers and abutments, approaches. The next step of this study was creating, training, validating and testing ANN models. The network with ADAM loss function and softmax activation showed the best results. The optimal values of MAPE and R2 were achieved at the 100th epoch with 64 neurons in the hidden layer and were equal to 0.1% and 0.99998, respectively. The practical application of the ANN models was carried out on the most common type of bridge in Ukraine, namely, a road beam bridge of small length, made of precast concrete. The novelty of this study consists of the development of a tool based on the use of ANN model, and the proposal to modify the methodology for quantifying the condition of bridge components. This will allow minimizing the uncertainties associated with the subjective judgments of experts, as well as increasing the accuracy of the assessment.

The geography of Kazakhstan is characterized by a diverse landscape and a small population. Therefore, certain automobile roads pass through unpopulated mountain regions, where physical road diagnostics are rare or almost absent, while landscape factors continue to affect the road. However, modern geo-information approaches and remote sensing could effectively provide the road diagnostics necessary to make timely control decisions regarding a road’s design, construction, and maintenance. To justify this assumption, we researched the deformation of a mountain road near Almaty city. Open access satellite images of and meteorological archival data for the region were processed. The resulting data were compared to validate the road’s deformation triggers. Extreme weather conditions’ impacts could be identified via road destruction (nearly 40 m longitudinal cracks, 15 m short transversal cracks, and two crack networks along a 50 m road section). The remotely sensed parameters (vertical displacement velocity, slope exposure, dissections, topographic wetness index, aspect, solar radiation, SAVI, and snow melting) show the complexity of triggers of extensive road deformations. The article focuses only on open access data from remote sensing images and meteorological archives. All the resulting data are available and open for all interested parties to use.

A first-time common cross-border assessment of observed climatic changes in the Saxon–Bohemian region was the aim of the German–Czech climate cooperation INTERKLIM. This paper focuses on the observed changes of temperature and precipitation averages and extremes within the period 1961–2010, investigating how variations of a range of climate indices were regionally shaped by changes in frequency and character of weather types. This investigation serves to enhance our understanding of the regional climate characteristics to develop transboundary adaptation strategies and focuses on the classification of the “Grosswetterlagen” using the parameters of air temperature and precipitation. Climate data were quality controlled and homogenized by a wide range of methods using the ProClimDB software with a subsequent comprehensive regional visualization based on Geographical Information Systems. Trends for the temperature averages showed increasing trend values mainly from January to August, especially for high temperature extremes. Precipitation trends displayed regionally varying signals, but a strong spatially uniform decrease from April to June (early growing season) and a distinctive increase from July to September (late growing season). Climatic changes were supported by frequency changes of weather types, e.g., the drying from April to June was related to a decrease/increase in patterns causing rather wet/dry conditions, while from July to September opposite trends were observed. Our results represent regional climatic changes in a complex topography and their dependency on variations in atmospheric circulation peculiarities.

Post-harvest crop residues are an interesting raw material for the production of composite materials. However, their surface often contains waxy and siliceous substances, which can cause adhesion problems. Therefore, various surface pre-treatment methods have been developed to increase the surface tension of these particles and hence to improve adhesive adhesion. The influence of hydrothermal, chemical, plasma and enzymatic treatment was investigated. The aim of the paper is to evaluate the effect of pre-treatments of post-harvest crop residues on the nature of joint failure and adhesive dispersion on the particles. The evaluation is based on microscopic analysis of particles obtained from the rupture area after internal bonding tests. The nature of bond failure and adhesive dispersion on the particle surface is evaluated. The results show a clear influence of material pre-treatment on the failure bond of the bond and, to a large extent, correlate with the mechanical properties of composites published in previous studies. The most suitable treatment appears to be a plasma treatment at a properly adjusted intensity. Conversely, the unsuitable treatment was alkaline, which, although it increased adhesion, deteriorated the overall mechanical properties. Hydrothermal treatment could be also considered as an industrially suitable method.

Diffuse Water Pollution from Agriculture (DWPA) and its governance has received increased attention as a policy concern across the globe. Mitigation of DWPA is a complex problem that requires a mix of policy instruments and a multi-agency, broad societal response. In this paper, opportunities and barriers for developing co-governance, defined as collaborative societal involvement in the functions of government, and its suitability for mitigation of DWPA are reviewed using seven case studies in Europe (Poland, Denmark, Sweden, The Netherlands and UK), Australia (Murray-Darling Basin) and North America (State of Minnesota). An analytical framework for assessing opportunities and barriers of co-governance was developed and applied in this review. Results indicated that five key issues constitute both opportunities and barriers, and include: (i) pressure for change; (ii) connected governance structures and allocation of resources and funding; (iii) leadership and establishment of partnerships through capacity building; (iv) use and co-production of knowledge; and (v) time commitment to develop water co-governance.

The research presented in this paper explored multiple objectives. First, what are the requirements for establishing a new composting business that embraces the principles of circular economy? Second, how can employment opportunities for at-risk youths from the most impoverished neighborhood in Guatemala City be created, while adhering to the tenets of social sustainability, of which human rights is the corner stone? Third, what were the requirements involved in making compost in the challenging climatic conditions of Guatemala City? And finally, from an educational perspective, how can this be taught to university students incorporating community service learning in its pedagogy, coupled with the model of action research? What are the obstacles to overcome when initiating a startup business, balancing what appeared to be a mix of complex economic, environmental, and social elements? These three elements are the recognized pillars of sustainability, and as such, there existed a great opportunity to meld the principles of circular economy, community service learning, and action research within the context of putting theories into practice. This applied research attempted to explore how effectively this could be accomplished in Guatemala while overcoming complex cultural, environmental, and economic barriers.

Organizations in general, and higher education institutions in particular, had to face many challenges during the pandemic in order to carry out their usual activities. Since communications and interactions between universities and students were required to take place online, the messages sent by universities on their social media platforms held greater importance. In this regard, the purpose of the paper was to identify the promotion strategies used by European universities on Facebook and Instagram in the context of the COVID-19 pandemic. Thus, we were interested in identifying differences and similarities in the types of messages European universities sent on the two social networks, regarding the way they communicated about sustainability. The method used in order to conduct the research was content analysis, while having as an instrument a content analysis grid. We analyzed the activity of 20 European universities included in the World University Rankings 2021. The results of the research revealed that universities communicated more on Facebook than on Instagram, that the most frequent messages regarding sustainability were the ones about the actions carried out by universities in order to protect the environment, and that they registered a higher engagement rate on Instagram than on Facebook.

Although more and more reliability-oriented sensitivity analysis (ROSA) techniques are now available, review and comparison articles of ROSA are absent. In civil engineering, many of the latest indices have never been used to analyse structural reliability for very small failure probability. This article aims to analyse and compare different sensitivity analysis (SA) techniques and discusses their strengths and weaknesses. For this purpose, eight selected sensitivity indices are first described and then applied in two different test cases. Four ROSA type indices are directly oriented on the failure probability or reliability index beta, and four other indices (of a different type) are oriented on the output of the limit state function. The case study and results correspond to cases under common engineering assumptions, where only two independent input variables with Gaussian distribution of the load action and the resistance are applied in the ultimate limit state. The last section of the article is dedicated to the analysis of the different results. Large differences between first-order sensitivity indices and very strong interaction effects obtained from ROSA are observed for very low values of failure probability. The obtained numerical results show that ROSA methods lack a common platform that clearly interprets the relationship of indices to their information value. This paper can help orientate in the selection of which sensitivity measure to use.