Ecosystem services (ES) have been widely researched for several years, but cultural ecosystem services (CES) have gained importance in recent years due to pressure on human well-being and public health. This literature review aims to continue research about ES assessment methods used in the valuation of landscape quality, analysing methods for CES assessment. The most assessed CES is aesthetic value being one of the most appreciated and widely known CES. Landscape quality assessment is complicated and rarely assessed, but several methods evaluate separate features of landscape quality, which gives an insight into accessible methods for landscape valuation. In this article, methods are analysed and categorised into four groups – economic, spatial evaluation, social and statistical analysis. Most analysed articles use several methods in one assessment giving more precise results. In CES assessment, the most used method is surveys and questionnaires and usually incorporating mapping methods to spatially explicitly represent the data.

Natural processes and human activity play a crucial role in altering the nitrogen cycle and increasing nitrogen oxide (N2O) emissions. Nitrous oxide isotopes 15N and 18O are important parameters that can help to explain the sources of N2O gas, as well as their circulation under different soil physical properties. The main goal of the study is to analyse the possibilities of using dinitrogen isotopes 15N and 18O, measured in soil samples, for the identification of N2O sources. A total of 16 plots were sampled. Each soil sample was assigned a code. Wetting of the samples was carried out to create wet aerobic conditions and wet anaerobic conditions. N2O measurements were performed in laboratory conditions using the Picarro G5131-i device. The 15Nα and 15Nβ values obtained in the measurement data were used to calculate the δ15NSP and δ15Nbulk values. The obtained δ15NSP and δ15Nbul values were analysed using two methods – descriptive statistics and Kruskal-Wallis test. The test showed that there are statistically significant differences between δ15NSP values (p-value <0.0001), and δ15Nbulk there was no significant difference (p-value 0.885).

The transformation from a linear to a circular economy model is a political priority of the European Union to develop sustainable and more efficient raw materials and waste management. The wastewater sector is an important element in the circular economy with a great potential for resource recovery such as phosphorus recovery from waste streams. With this in view, the present study’s aim was to analyse data on municipal wastewater treatment plants (>2,000 p.e.) in Latvia and the present situation with newly developed circularity indicators on waste reduction, pollutants removal, resource recovery and water reuse; thus, enabling evaluation of the level of transformation towards circularity in Latvia’s wastewater treatment plants and their future perspectives. The results showed that the wastewater sector in the country complies with legal obligations, but on the other hand, only a few solutions are practised in compliance with the principles of circular economy, i.e., reduction of wastewater discharge and increasing sewage sludge utilization as fertilizer in agriculture.

The paper presents the results of the research on oil spill detection using machine learning methods such as Support Vector Machine (SVM) for classification of infrared images and Logistic regression for water quality parameters. This paper focuses on real time detection of oil spills using infrared images and water quality data obtained by RPA equipped with multi-sensor payload. The developed Naïve Bayes (NB), SVM and Logistic regression classification models for prediction of oil spill have been successfully tested in real experiment conditions. All developed classification models were tuned using grid search method and main tuning parameters to determine the optimal parameters. The proposed complex algorithm for identification of oil spills using infrared images and water quality parameters is evaluated by experiments in real environment conditions. The proposed algorithm is based on the binary SVM and NB classification of infrared images and the classification of water quality parameters using the machine learning method logistic regression allows to rapidly and with high accuracy identify any oil pollution of water. Proposed complex algorithm achieves higher accuracy and efficiency; moreover, the developed machine learning models will further reduce the probability of human error and save man-hours of work.

Stable isotope ratios of water (δ2H and δ18O) are important indicators which describe hydrological processes in the environment. These parameters allow to analyse structure, status, migration of pollutants and processes of the groundwater system. Groundwater from dug wells is widely used as a drinking water source highlighting the importance of investigation of water quality and its possible flows and sources. Despite available centralized water systems, there are still wide territories where dug wells are the main source of drinking water supply. Dug wells are recharged from shallow groundwaters which makes them more vulnerable to anthropogenic contamination. Therefore, quality monitoring and estimation of anthropogenic influences are of importance. Quality requirements of drinking water do not provide measurements of stable isotope ratios in drinking water, but the combination of those elements can be valuable for the characterization of impacts on groundwater quality. The aim of this study was to survey 64 dug wells in Eastern Latvia and analyse water quality together with measurements of stable isotope ratios. Measurements were performed twice, i.e., autumn and spring to track water sources in wells and describe the extent of possible impacts. This approach can indicate the magnitude of local factor impacts on drinking water quality. Surveyed wells provide high differences in analysed parameters, for example, nitrate concentrations in autumn varies from 0.44 to 108.26 mg LE−1. Also, other detected parameters are variable; therefore, tracking water flow with stable isotope values gives better insight into the water quality of the studied region.

Cyberattacks have become a regular part of network activity. To mitigate the risks from possible threats, organisations have implemented firewalls and intrusion detection systems, which can help stop network attacks. The problem is that often the accuracy of these systems is not effective enough. Another part of network security is security information and management platforms. These systems are more advanced versions of Threat Intelligence Platforms, because it is possible to make in-depth analyses of real-time events in a network. This research paper proposes improving intrusion detection system functionality using Open-Source Intelligence. Anomaly-based intrusion detection systems often generate alerts, but these alerts require deeper analysis to understand whether it is a real attack or just a false alarm. By making Open-Source Intelligence requests and evaluating extra information, it is possible to make more precise rules to stop attacks against network infrastructure. Open-Source Intelligence requests are generated directly from the intrusion detection system or with Python scripts based on the organisation’s infrastructure profile. The proposed architecture was experimentally tested by automating Open-Source Intelligence requests and intrusion detection rule generation by Python scripts.

One of the most important problems of modern education is low achievement in STEM (Science, Technology, Engineering and Mathematics) subjects. Poor performance in mathematics is a serious problem in many countries. In order to improve the effectiveness of mathematics studies, it is more important to improve such key factors as students’ learning self–efficacy and learning initiative. This study is devoted to students’ learning initiative and the factors influencing it. It is the insufficiency of students’ regular learning initiative in the study process that is the basis of the fact that students are not sufficiently prepared for the final exam. Based on the research results of the scientific literature, a questionnaire was created that investigated the students’ learning initiative and factors that could improve it – attitude towards mathematics studies, motivation, as well as emotional factors that are often related to Causal attributions. The research was conducted at Latvia University of Life Sciences and Technologies, surveying students of various specialties and various courses. Fisher’s exact test of independence is used to determine whether there is a significant relationship between two categorical variables – the respective factors and performance on the math completion test. The results show that one of the main causes is a low self–assessment of mathematics competence, which in turn affects motivation and attitude towards mathematics studies and slows down learning initiative.

Today in Ukraine, a digital transformation of the educational system of higher education is taking place at a rapid pace, in which information and communication technologies are not becoming an additional ʻload’ in learning, but an integral part of a comprehensive educational process, which increases its efficiency. At the same time, there are a number of challenges, including such specific ones as unprovoked military aggression, which complicates the process of entry of higher education in Ukraine into the global educational digital environment. To assess the significance of the digitalization of the educational system of higher education in Ukraine in specific conditions, to analyse the attitude of the teaching staff and students to online education, to establish technical means of participation in the educational process, to determine the advantages and disadvantages of distance learning through the prism of the experience of teaching staff and students, we used three main methods: the survey method, the method of analysis and synthesis, the graphical method. As a result of the study, the authors revealed the general concept of digitalization and its educational dimension; identified the main challenges faced by higher education institutions in the digital environment; determined the impact and scale of military aggression by the Russian Federation on the activities of Ukrainian universities; established the features of training in the digital environment by institutions of the higher education of Ukraine.

Individuals’ emotional well-being is determined by several factors, including the ability to cope with daily stress and the ability to cope with various daily challenges. Ability of the management to provide favourable psychoemotional and psychosocial conditions within their team is particularly important. However, managers do not always have the knowledge and skills to lead their teams. For the development of the methodology, a study of the scientific literature on topical issue like healthy workplace was performed. The empirical part of the study is based on the results of the survey conducted Lithuania, Latvia, Estonia and Sweden within the Nordplus project ‘Dealing with anxiety during a pandemic to enhance adult well-being’ on the impact of COVID-19, as well as the training that would be necessary for the managers of companies or organizations to promote emotional wellbeing in the workplace. The comparative study was conducted from the perspective of both employees and managers. The results show that the impact of the COVID-19 pandemic on workplace and the well-being of employees was not strong for the respondents, depending mainly on the job position, the size of the organization and country. Managers and employees consider various psychological trainings essential.

Globally crop pathogens and pests cause significant yield and quality losses in agriculture production systems. Foliar fungal diseases of small grain cereals are economically among the most important diseases worldwide and in the Baltics. Finding an effective, reliable, and easily accessible method for plant disease diagnosis still presents a challenge. Currently used methods include visual examination of the affected plant, morphological characterization of isolated pathogens and different molecular, and serological methods. All of these methods have important limitations, especially for large-area applications. Hyperspectral imaging is a promising technique to assess fungal diseases of plants, as it is a non-invasive, indirect detection method, where the plant’s responses to the biotic stress are identified as an indicator of the disease. Hyperspectral measurements can reveal a relationship between the spectral reflectance properties of plants and their structural characteristics, pigment concentrations, water level, etc., which are considerably influenced by biotic plant stress. Despite the high accuracy of the information obtained from hyperspectral detectors, the interpretation is still problematic, as it is influenced by various circumstances: noise level, lighting conditions, abiotic stress level, a complex interaction of the genotype and the environment, etc. The application of hyperspectral imaging in everyday farming practice will potentially allow farmers to obtain timely and precise information about the development of diseases and affected areas. This review provides an introduction into issues of hyperspectral imaging and data analysis and explores the published reports of worldwide research on the use of hyperspectral analysis in the detection of foliar fungal diseases of small-grain cereals.