The energy-intensive timber industry enterprises have a significant role in Latvia’s manufacturing sector, contributing to the country’s sustainable economic development. They maintain the economic well-being of residents of Latvia’s rural regions and represent a substantial proportion of output value and export volume in the manufacturing sector and the state economy. Due to surging energy prices that negatively impact production costs, it is more difficult for enterprises to compete in foreign markets. The study aims to analyse energy consumption trends in timber industry enterprises in Latvia to propose activities for reducing energy costs, thus promoting the international competitiveness of timber industry enterprises. The analysis is based on data from Europe’s power market Nord Pool and databases of the Official Statistics Portal of Latvia. Methods used: statistical data analysis using time series analysis and expert interviews. The research results show that a significant threat to the competitiveness of enterprises is the increasing price of electricity, which is the second most consumed energy resource, accounting for nearly 15% of the total annual energy consumption of timber industry enterprises. To address high electricity costs, enterprises have the opportunity to install solar panels. Calculations show that, at an electricity price of 0.198 EUR per kWh, investments could lead to a five-year payback period with an ROI of 7.52%. At a lower electricity price of 0.13 EUR per kWh, the payback period would extend to ten years but still provide a reasonable ROI of 5.2%.

One of the most pressing problems of sustainable spatial development in Latvia is the existence of degraded areas. To address the issues of revitalization of degraded territories and to ensure sustainable development of the territory, Latvian municipalities could apply for support under the Specific Support Objective 5.6.2 (SSO 5.6.2) ‘Revitalization of territories through regeneration of degraded territories in accordance with integrated development program of municipalities’ during the European Union (EU) Structural Funds programming period 2014–2020. According to the Ministry of Finance (MF) data on project costs, it was found that during the implementation of the projects for revitalization of degraded territories, the local governments have not ensured an effective project management process in accordance with the implementation requirements of SSO 5.6.2, resulting in irregularities detected in the audits carried out on the projects, which have led to the decision on inappropriately performed expenses (IPE). Considering that the deadline for the implementation of projects under the 2014–2020 EU funds programming period is 31.12.2023, it is necessary to carry out an analysis of IPE to identify the reasons for the occurrence of IPE and to plan timely preventive actions to eliminate mistakes in the new programming period 2021–2027. The relevance and importance of the study lies in the analysis of the factors holding projects back - mistakes and irregularities and their prevention to ensure an efficient project management process.

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.