The article argues for the inevitable transformation of the soldier from a tool user to an integrated weapon system – Soldier 3.0 – constituting a new arena in the global arms race. This evolution is driven by the convergence of four key domains (NBIC): nanotechnology, biotechnology, information technology, and cognitive science. The article cites the foundational U.S. Department of Defense report, Cyborg Soldier 2050: Human/Machine Fusion and the Implications for the Future of the DOD, as a strategic forecast for this transformation. The analysis identifies three main areas of technological advancement: physical, cognitive/psychological, and sensory. These advancements constitute a key element of advantage and are fundamentally redefining the nature of future armed conflicts, dangerously blurring the line between human being and strategic asset.
This paper investigates the strategic deployment of disinformation as a tool of influence operations, focusing on Russian campaigns targeting perceptions of Ukrainian refugees in Poland and the EU. Utilizing the Diamond Model for Influence Operations and supported by Cyber Threat Intelligence analysis from Recorded Future, the study identifies key actors, audiences, capabilities, and infrastructure involved in these malign efforts. The authors argue that disinformation is not merely a spread of falsehoods, but a deliberate manipulation of narratives designed to exploit technological affordances and societal vulnerabilities. The research highlights how emotionally charged content, algorithmic amplification, and coordinated bot activity contribute to the virality and perceived credibility of disinformation. The case illustrates how Russia seeks to fracture European solidarity, delegitimize Ukraine, and erode public support for humanitarian and military aid. The findings underscore the importance of societal resilience, advocating for enhanced media literacy, institutional trust, and multi-level countermeasures. Ultimately, the paper concludes that combating influence operations requires a holistic approach that integrates technological, regulatory, and educational strategies to mitigate the impact of strategically crafted disinformation.
This article contains two selected properties of foamed polypropylene. Durability and water absorption were tested. The tested possibilities are available in terms of the use of the tested material in air structures. Resistance to them is possible due to the fact that they are structural elements of air elements, while the lowest water absorption is required due to the requirements that can be operated by airborne vehicles. The property consisting in the ability to absorb water by the tested material excludes it from the test results and its use in the formation of air substances. The test results do not provide grounds to reject the tested material as a potential use of a structural material in air structures.
The paper examines how meteorological conditions affect all components of the DTII chain (Detection–Tracking–Identification–Incapacitation) in counter-UAS operations and tests whether the Russian Armed Forces deliberately time mass Shahed strikes to exploit favorable atmospheric phenomena. The review section summarizes the weather vulnerabilities of radar, EO/IR, acoustic, and RF sensing, highlighting near-surface temperature inversions and radar ducting (range extension/radar “holes,” increased false echoes, EO/IR degradation, and relatively improved acoustic propagation). The research section performs a hindcast analysis of strikes on Ukraine from August 1, 2024 to September 15, 2025 (strike counts from official Ukrainian releases/Telegram; weather from ICON and ERA-5 reanalyzes). “Salient strikes” are defined as episodes exceeding at least twice the monthly mean number of UAS. Over that period, 44,390 UAS were launched and 26,701 neutralized (~60%). Of 158 strikes with >100 UAS, 78% occurred under inversion; all 39 strikes with >200 UAS took place during inversion conditions. From February 2025 onward, the correlation is unambiguous: inversion days represent roughly half of the calendar but account for 76% of all launched UAS (February to September). City-level results show that 25/26 (96%) of Kyiv’s largest strikes, 14/15 (93%) in Odesa, and 8/8 (100%) in Lviv coincided with inversions. We infer systematic Russian selection of “inversion windows,” even though reported Ukrainian C-UAS effectiveness (~60%) does not markedly drop on those days. We recommend multi-sensor fusion, operational “risk-window” forecasting, and institutionalizing an “owning-the-weather” approach in C-UAS planning, alongside continued validation of system thresholds and methods.
Road accidents pose significant challenges to public safety and necessitate proactive measures to mitigate them. This paper introduces a machine-learning approach for predicting road accident incidences, leveraging diverse datasets encompassing traffic patterns, weather conditions, and historical accident records. The proposed model integrates feature engineering techniques to capture the multifaceted nature of variables influencing accidents. Through the application of advanced machine learning algorithms, such as ensemble methods and neural networks, the model aims to discern complex patterns within the data, facilitating accurate predictions of accident likelihood. The study also explores the interpretability of the model outputs, providing insights into the key predictors and their interactions. Validation and performance assessment involve rigorous testing on diverse datasets to ensure the generalizability and robustness of the predictive model. The outcomes of this research hold promise for the development of proactive road safety strategies and the implementation of targeted interventions, ultimately contributing to reducing road accidents and their associated societal impacts.
