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.

Satellite communication has become an indispensable technology for modern society, providing global connectivity and enabling a wide range of applications such as navigation, weather forecasting, and data transmission. However, the efficiency and security of satellite communication channels are still challenging issues that require continuous improvement. This article presents recent advances and the state of art in satellite communication technology. Specifically, we discuss the RF power flux density, received power and received signal power. Using the RF PROP application, we compared the link characteristics of the leading satellite service providers depending on whether they broadcast from geostationary, medium, or low Earth orbit.

A review of scientific works published over the last 3 years concerning the issue of immunological changes and related perinatal outcomes in pregnant women with COVID-19 was carried out. It was found that pregnant women are more likely to have a severe course of COVID-19. This is largely due to immune changes in the pregnant woman’s body, as well as to the pathophysiology of COVID-19. This article discusses the main immunological changes, such as reduced levels of natural killer cells (NK cells), type 1 T-helper cells (Th1), plasmacytoid dendritic cells (pDCs), decreased phagocytic index of neutrophilic granulocytes and monocytes, as well as immunomodulatory properties of progesterone, the content of which is increased in pregnancy. Factors that may exacerbate the course of the disease due to hyperinflammatory states include an increase in the complement system, which is associated with more severe lung injury, and an increase in TLR-1 and TLR-7 toll-like receptors that bind to the virus, leading to increased production of pro-inflammatory cytokines such as IL-6 and TNF-α, which are elevated in the normal physiology of pregnancy. This leads to a higher incidence of pre-eclampsia, eclampsia, preterm labor and other adverse outcomes in pregnant women with COVID-19.

Hygrothermal assessment plays a critical role in the design and maintenance of healthy, energy-efficient buildings. Despite established knowledge of condensation mechanisms and mitigation strategies, condensation and moisture remains a persistent issue even in newly constructed structures. This ongoing challenge highlights the need for empirical validation of data critical to condensation occurrence. This study presents the development and evaluation of a mobile, on-site measurement system designed to collect data on surface condensation and thermal conductivity of building walls. The system is developed using a data acquiring and processing platform myRIO built around LabVIEW, enabling real-time detection of critical condensation conditions and deviations in thermal conductivity from measured values. Measurement results were validated with the Heat Flow Method (HFM) and theoretical calculations at the same site.

The rapid advancement of artificial intelligence (AI) is transforming naval capabilities, reshaping ship design, lifecycle management, operational decision-making, and autonomous maritime systems. Naval platforms are among the most complex engineered systems, characterised by long service lives, safety-critical functions, and demanding operational environments, making AI integration both strategically attractive and technically challenging. This paper presents an engineering-oriented review of AI applications in the naval domain, focusing on their role across the capability development lifecycle. To illustrate practical implementation, a Random Forest regression model is developed to support early-stage prediction of the block coefficient of naval ships. The review highlights significant opportunities associated with AI integration, including enhanced decision-making, improved design efficiency, and increased operational effectiveness. However, successful AI adoption requires technological advancement alongside organisational adaptation, strong governance, and sustained investment in human expertise. AI should therefore be understood not as a replacement for naval engineering expertise, but as a force multiplier that augments analytical capacity and accelerates innovation across the maritime domain.