This paper provides an overview of key methods for assessing the quality of measurement systems and measurement results. Fundamental concepts are analyzed and their definitions compared across various metrological standards. Particular emphasis is placed on the issue of terminological inconsistency, as the same terms are interpreted differently in different documents, which may lead to misinterpretation or incorrect application of methods. This paper also presents practical examples of how these methods are applied to real data. Clear indications are provided regarding the conditions under which each method is recommended, depending on factors such as model complexity, data availability, and intended application. In addition, it offers a discussion and recommendations for future directions, highlighting the need for harmonized terminology, standardization of evaluation procedures, and the adoption of advanced technologies such as artificial intelligence and machine learning in the assessment of measurement quality.

Theoretical U-values, which measure thermal transmittance, can be calculated based on the thermal parameters of an opaque element’s layers. However, practical measurements are essential to validate these theoretical values. The heat flux meter (HFM) method, is a widely accepted standard for such measurements. Despite its prevalence, the HFM method faces challenges, including wall surface roughness, ensuring proper contact between measurement devices and surfaces, and weather-related fluctuations. This study introduces a prototype system that employs a modified temperature-based method (TBM) to address these challenges. The paper provides a detailed comparison of thermal transmittance measurements obtained using both the modified TBM and the HFM method. The results showed U-value differences between the two methods. Additionally, these experimental findings were compared with theoretical calculations, highlighting the efficacy and potential of the modified TBM as an alternative approach for accurate U-value determination.

In the context of quality management and Industry 5.0, reproducibility is a critical indicator of the ability to achieve consistent and predictable results in laboratories, research, as well as in systems and processes. Reproducibility is also an indispensable component of measurement uncertainty, which quantifies the precision of measurement results. To standardize the measurement procedure for surface topography parameters obtained using atomic force microscope (AFM) and to establish the accuracy and precision of measurement results, an analysis of repeatability and reproducibility was conducted in accordance with the ISO 5725-2:2019 standard, Accuracy (trueness and precision) of measurement methods and results – Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method. Measurements were performed on two AFM standards and a steel sample. The research was conducted over the course of one year and repeated after four years using the same samples.

The study examines the methods and effectiveness of Russian propaganda during the Russo-Ukrainian war, focusing on three specific cases: the declared number of casualties, the denial of the Bucha massacre, and the dirty bomb narrative. The success of Russian propaganda is evaluated through the level of support for the “special operation” in Ukraine. Three cases of propaganda were evaluated thro ugh five criteria. To investigate their efficiency, propaganda cases were subjected to indirect assessments, leveraging secondary sources. Finally, secondary data on the Russian public’s support for activities in Ukraine and some views on the media were presented, which in this analysis provide additional argumentation to the pre viously analysed cases. The analysis assesses whether only emotional manipulation techniques were used or whether additional tactics were employed. The technical quality of the individual propaganda campaigns is also assessed. Despite the be low-average quality of the individual campaigns, the study shows that Russian pro paganda achieves its goals by evoking emotional reactions. In particular, it finds greater support among older, less educated people who rely primarily on televisi on programmes (which are largely controlled by the central Kremlin authorities). Although the propaganda also has an impact on younger viewers, its influence is comparatively weaker. Ultimately, the study considers Russian propaganda to be successful because it is able to generate significant emotional engagement among the domestic public and thus contribute to support for the war.

This paper systematically presents the disruptive technologies that have emerged on the battlefields in recent decades, as well as those that are yet to come. Special attention is given to current technical capabilities: the status of unmanned vehicle development is briefly outlined, focusing primarily on the most prevalent type, unmanned aerial vehicles (UAVs). Additionally, the paper discusses the most common and effective adversarial attack techniques specifically targeting unmanned vehicle technology. The concepts of artificial intelligence (AI), machine learning, deep learning, and convolutional neural networks (CNNs) are introduced. The paper illustrates how CNNs aim to tackle tasks that previously required human intelligence, as well as how the enemy attempts to disrupt the development of CNNs during the crucial training and pattern recognition phase, which is essential for later generalisation. The paper demonstrates the advantages of manned-unmanned teaming as a model that effectively utilises disruptive technologies while simultaneously counteracting the effects of the enemy’s measures. Moreover, it analyses the introduction of fully autonomous, AI-driven military systems on the battlefield, outlining the advantages and disadvantages inherent to such a fundamental change. From the evident lack of interest among young people in joining the armed forces to the autonomous systems’ potential to save the lives of soldiers and civilians, there are numerous reasons suggesting that this technology could alleviate the burden on human soldiers. However, concerns remain that autonomous systems may malfunction, potentially reducing rather than increasing the safety of militaries. The paper concludes with recommendations for future steps in the introduction of new technologies, based on their current state of development and the robustness of the AI models they use.