Search Results (1,684)

As unmanned aerial vehicles (UAVs) are increasingly deployed in mission-critical domains such as military operations, infrastructure inspection, and disaster response, the threat of GPS and network spoofing attacks has emerged as a fundamental challenge to operational continuity. Existing intrusion detection systems based on threshold rules or shallow machine learning models are inherently limited in their ability to identify the latent onset of sophisticated, gradually intensifying spoofing campaigns—a gap that motivates the present work. This study proposes a deep learning-based early detection and network resilience prediction framework that employs Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures operating on three spatio-temporal network features—Hop Count Spike Rate (HCS), Packet Drop Volatility (PDV), and Spatial Disconnect Density (SDD)—proposed in this study. To reflect realistic adversarial conditions, we design a Gradual Adaptive Attacker model in which the spoofing intensity escalates progressively across six operational phases, including a second-stage adaptive attack that modulates its gradient upon detecting initial countermeasures. Both models are trained on 1000 simulated runs using sliding-window time-series sequences and evaluated across 500 independent test runs with paired statistical testing. The GRU model achieves a mean ROC-AUC of 0.9915 (±0.0091) and a mean F1-Score of 0.9099 (±0.0462), outperforming LSTM across all metrics with statistical significance at p < 0.001 under both the paired t-test and the Wilcoxon signed-rank test. Critically, GRU detects spoofing onset with an average latency of 0.503 time steps—approximately 29.4% faster than LSTM (0.712 steps)—a difference confirmed as statistically significant (p < 0.001, Cohen’s d = 0.41). Network resilience simulations further demonstrate that integrating GRU-based autonomous evasion maintains a Connectivity Ratio (CR) above 80% even under severe attack phases, whereas passive networks experience total connectivity collapse (CR = 0%). These findings establish GRU as the superior architecture for real-time UAV edge deployment and affirm that the proposed pipeline extends beyond threat alerting to actively preserving mission continuity under adversarial spoofing conditions. Full article

Infrared small object detection (ISOD) plays a crucial role in military reconnaissance, security surveillance, and remote sensing monitoring, where weak thermal responses and complex backgrounds impose significant challenges. The recent self-supervised vision foundation model DINOv3 has demonstrated remarkable generalization ability across various visual tasks. However, directly transferring it to ISOD still remains challenging due to substantial cross-domain discrepancy between visible and infrared imagery, as well as the limited granularity of foundation features in capturing subtle thermal variations. To address these issues, this study proposes a spatial–frequency inductive bias-guided network (SFI-Net) based on DINOv3 for cross-domain representation learning in infrared small object detection. Instead of conventional domain adaptation strategies, SFI-Net explicitly models infrared-specific inductive biases in both spatial and frequency domains to enhance transferred representations. First, a spatio-frequency hybrid adapter (SFHA) is designed and embedded across multiple layers of the frozen backbone to learn infrared-specific inductive biases within distinct subspaces. Second, a feature compensation strategy with an auxiliary convolutional branch is devised to compensate for the limitation of DINOv3 in capturing multi-scale fine-grained features. Extensive experiments on the IRSTD-1K and NUDT-SIRST datasets demonstrate that the proposed SFI-Net outperforms state-of-the-art methods in both detection accuracy and computational efficiency while exhibiting strong cross-scenario generalization capability. Full article

Geopolitical tensions remain an important source of uncertainty for global oil markets. This study examines whether recent geopolitical shocks related to escalating tensions in the Middle East in 2025–2026 were associated with changes in oil price volatility regimes. The analysis is based on daily WTI crude oil prices covering the period from 1 January 2024 to 10 April 2026. A two-regime Markov-switching GARCH model is used to identify low- and high-volatility states. The regime classification is further supported by return-variance tests, episode-level descriptive statistics, and a sensitivity analysis of alternative probability thresholds. The results show that the oil market remained in a low-volatility regime for most of the sample, but three distinct high-volatility episodes were identified, i.e., in early April 2025, June 2025, and late February to April 2026. These episodes differed in duration, direction, and intensity. The 2026 episode was the longest and most persistent high-volatility period, with the highest conditional volatility, the highest average probability of the high-volatility regime, and the widest daily price ranges. The sensitivity analysis confirms that the identification of the three main episodes is robust to stricter probability thresholds. The findings suggest that recent geopolitical shocks coincided with distinct volatility regime episodes in the oil prices, with direct military escalation in the Middle East being associated with the strongest and most persistent market turbulence. Full article

This study is part of a research program at the Military University of Technology aimed at creating a tool to support light aircraft design at the conceptual stage. The project seeks to develop a method for optimizing a conceptual model of a small manned or unmanned aircraft based on specific mission requirements and aerodynamics. Recognizing the need for a reliable CFD analysis tool in this process, the focus was placed on investigating popular tools utilizing panel methods. Full article

Background: Despite well-documented adverse impact on both systemic and oral health, tobacco smoking remains a persistent issue in military populations. It contributes to the global burden of tobacco use and is often perceived as a means of coping with stress in military settings. Purpose: This study aimed to assess the prevalence of tobacco use among military marines, its impact on oral health, and their level of knowledge regarding smoking, as well as to identify variables associated with their smoking habits. Thus, it provides a basis for implementing appropriate tobacco cessation and harm reduction strategies, particularly within the military. This study demonstrated a high prevalence of tobacco use among military marines, despite generally high levels of knowledge regarding tobacco smoking. A knowledge gap was still evident in relation to smoking behavior. The most frequently reported oral health impacts among smokers were tooth staining, halitosis, and taste impairment. Duration of military service and level of knowledge were significantly associated with smoking behavior. Materials and Methods: A validated and reliable online survey was administered to collect socio-demographic data, including age, education level, and length of military service. The survey also assessed smoking status, smoking behavior, its impact on oral health, and participants’ knowledge of smoking-related risks. Data were analyzed descriptively, and associated factors were examined using multivariate analysis. Results: A total of 475 military marines participated in the study. Of these, 44.8% were current smokers, 25.7% were former smokers, and 29.5% had never smoked. Overall, 71% of participants demonstrated good knowledge of smoking-related risks. The most commonly reported oral health impacts were halitosis, tooth staining, and impaired taste. Smoking status did not differ significantly by age (p = 0.095) or education level (p = 0.610), but differed significantly by length of military service (p < 0.05) and level of knowledge (p < 0.05). Multivariate analysis using multinomial logistic regression indicated that length of military service was a significant predictor of smoking behavior (p = 0.005; 95% CI: 0.282–0.800), with 1–5 years of service emerging as the most influential category. Based on the odds ratio, individuals with 11–15 years of service had a 1.8-fold higher likelihood of smoking. Conclusions: Despite a generally good level of knowledge regarding the health risks of smoking, the prevalence of tobacco use remains high among military marines. The most commonly reported oral health impacts were tooth staining, halitosis, and impaired taste. Length of military service and level of knowledge regarding smoking were identified as significant factors associated with smoking status. Full article

Infrared small target detection is widely used in aerospace surveillance, maritime search and rescue, and military reconnaissance. However, the performance of detection algorithms is highly dependent on scene characteristics, and methods that perform well in simple backgrounds may degrade substantially in complex environments. Existing indicators, such as information entropy, average gradient, and peak signal-to-noise ratio, can reflect detection difficulty from individual perspectives, but they do not provide a unified measure that jointly considers target saliency, background complexity, and target–background coupling. To address this issue, this study proposes a scene detection complexity (SDC) metric for quantifying the difficulty of infrared small target detection. Six basic indicators are selected from three dimensions, namely target saliency, background complexity, and target–background coupling: statistical variance, target–background contrast, signal-to-clutter ratio, information entropy, structural similarity, and target size. After Min–Max normalization, objective weights are determined by combining the entropy weight method and principal component analysis, and the weighted indicators are fused into an SDC value in the range of $[0,1]$. Experiments on 100 test images selected from IRST640, MSISTD, SIRST-V2, and an infrared small-aircraft sequence dataset show that the proposed SDC achieves a Pearson linear correlation coefficient of 0.956 with subjective difficulty ratings and $-0.902$ with image-level detection scores obtained from seven representative algorithms. The results further indicate that traditional methods are more sensitive to increasing scene complexity, whereas deep-learning-based methods are comparatively more robust in complex backgrounds. The proposed SDC provides a unified and objective tool for performance evaluation, algorithm selection, and pre-assessment of scene difficulty in infrared small target detection. Full article

The ultimate goal of the secession movements is to gain recognition in accordance with international law, thereby strengthening its legitimacy on the international stage. The intensity of the conflict influences the likelihood of the movement being addressed within the framework of Countering Violent Extremism (CVE) discourses, thereby shaping its international legitimacy. This article examines how the international legitimacy of secession movements is shaped by conflict profiles, religious significance and the CVE framework. Moving beyond the tendency to treat long-standing separatist conflicts as inherently religious, religion can enter separatist conflicts in various ways, such as being an indicator of collective differentiation, a language of mobilization, a source of symbolic legitimacy or an external framework of interpretation. In this study, international legitimacy is conceptualized as existing beyond formal recognition and is assessed using four indicators: discursive, diplomatic, institutional and support-based legitimacy. Five African case studies (Western Sahara, Cabinda, Biafra, Azawad and Ogaden) are detailed within the context of these indicators, having been selected for sharing similar values regarding conflict based on variables derived from the Uppsala Conflict Data Program (UCDP). Ultimately, in the cases of Western Sahara and Azawad, associating religion with extremism undermines the legitimacy of separatist claims and restricts access to international policy and military support. Full article

The exhaust gas temperature (EGT) of the gas generator is a critical indicator for the health management system of a marine gas turbine engine. Therefore, EGT prediction can not only support predictive maintenance decision-making but also serves as a reliable virtual sensor for EGT measurement. However, the engine EGT exhibits strongly nonlinear coupling relationships with other gas path variables, which causes challenges for data-driven prediction. Graph neural networks (GNNs) are particularly effective in capturing the coupling relationships among gas path sensor variables. However, conventional static graph structures fail to characterize the varying coupling strengths under different operating conditions. In this study, a thermodynamic knowledge-driven graph attention network (TKD-GAT) method is proposed for accurate and robust EGT prediction. First, a physics-guided graph topology is constructed based on the gas turbine thermodynamic equations. Subsequently, a multi-head attention mechanism is introduced to generate edge weights that capture the varying thermodynamic coupling strengths under different operation conditions. The proposed model is evaluated on a real-world LM2500 gas turbine, which is widely used in modern propulsion systems of commercial and military ships. The ablation study confirms that the thermodynamic knowledge-driven graph topology and the attention mechanism-based edge weights are both necessary to enhance the EGT prediction performance. The TKD-GAT model shows the best performance with an RMSE of 0.446% and an R² of 0.971 compared with state-of-the-art models. The paired t-test and effect size measurement (Cohen’s d) statistically confirm the significance of performance improvements. The statistical results from multiple independent experiments prove the stability of the TKD-GAT model. Additionally, the model achieves a competitive computational cost despite the integration of a physics-guided graph topology and attention mechanisms. Crucially, an interpretability analysis confirms that the learned attention weights adhere to thermodynamic principles under different operation conditions. The proposed TKD-GAT model provides an effective solution for EGT prediction in health management systems. Full article

Working memory, the capacity to store information for near-immediate use, and visual attention, the ability to focus on task-relevant information, are integral skills for military personnel. In civilian populations, stress is associated with worse skills. However, little is known about the relationship between stress, working memory, and visual attention in military officers, who are trained to handle acute stress and operate in high-stress environments. Thirty-three military officers completed a working memory test, a Perceived Stress Questionnaire (PSQ), and an oculometric assessment of visual tracking. The oculometric test was a modified step-ramp test that produces 10 z-scored metrics. Working memory and executive function were assessed via the n-back task. Oculometric performance and self-reported stress levels were independently associated with n-back accuracy, explaining 67% of the variance (adjusted R², n = 30). The association between oculometric performance and n-back accuracy was driven by directional anisotropy, directional noise and proportion of smooth pursuit. The association between oculometric performance and stress was complicated by sex differences. Results have important implications for the assessment of cognitive readiness in military populations. The strong relationship between oculometric performance and working memory suggests that eye-tracking-based metrics may serve as candidate indicators of cognitive function under operational demands. Full article

The rapid advancement of unmanned aerial vehicle (UAV) technology has led to its widespread adoption in military reconnaissance, disaster monitoring, environmental inspection, and related fields. However, a single UAV often faces limitations when executing large-scale and complex missions. UAV swarm technology, which employs multi-agent collaboration, can significantly improve task execution efficiency and overall system performance, representing an area of considerable research importance. Current studies on task allocation and path planning for UAV swarms exhibit certain shortcomings, particularly the high computational complexity and insufficient real-time performance of existing path planning methods when applied to highly dynamic, multi-objective, and large-scale complex scenarios. To address the above challenge, this paper proposes a Gale-Shapley-based Genetic Algorithm (GSGA) for UAV swarm task allocation and path planning. First, a multi-UAV data inspection system model is formulated based on an energy consumption model, analyzing the influence of factors including geographical fairness, data utility, and energy consumption. The proposed GSGA integrates the Gale-Shapley stable matching algorithm for one-to-one task assignment between UAVs and sub-regions with a genetic algorithm optimized for intra-region path planning. Dynamic programming is further employed to refine the flight paths. The results show that the GSGA strategy can effectively improve the balance of task allocation, optimize path length and inspection quality. The proposed method demonstrated robust performance in complex scenarios characterized by numerous task targets and intricate regional partitions, consistently enabling UAVs to complete inspection tasks with high collaborative efficiency. Full article

Effective management of infectious healthcare waste at the Military Medical Academy (VMA) depends on reliable forecasting in order to ensure adequate treatment capacity (e.g., sterilization facilities), optimize logistics, maintain regulatory compliance, and minimize environmental impact. However, conventional statistical approaches often struggle to capture the complex and heterogeneous patterns of waste generation observed across clinical departments with different medical specializations. The aim of this study is to develop and comparatively evaluate six models for predicting annual infectious waste generation across 24 clinical departments of the Military Medical Academy in Belgrade, Serbia. The analysis is based on an 11-year real-world panel dataset (2011–2021), which is further used to produce forecasts for the period 2022–2031. The modeling framework includes both traditional statistical methods (OLS, Ridge, and Lasso regression) and machine learning techniques (Random Forest, Gradient Boosting, and Multilayer Perceptron). Model performance is assessed using k-fold cross-validation and standard evaluation metrics (RMSE, MAE, and R²). The results indicate that machine learning models, particularly Gradient Boosting and Random Forest, achieve better predictive performance compared to traditional approaches. Although the findings are based on data from a single hospital complex, they offer a useful empirical basis for understanding and forecasting infectious healthcare waste in large, multi-department healthcare institutions. Full article

Underwater-transportation systems have significant potential for both military and commercial applications. Neural Network (NN)-based control offers enhanced robustness for actuators to manage the states of autonomous underwater-transportation systems which include Rigid-Connection Transportation Systems (RCTSs), Flexible-Connection Transportation Systems (FCTSs) and Leader–Follower-Formation Control Transportation Systems (LFFCTSs). In this study, NN-Augmented Control (NNAC) is applied to the aforementioned three transportation systems to enable accurate path tracking by the actuators installed onboard these systems under both ideal operating conditions and in the presence of sensor and process noise. The Extended Kalman Filter (EKF) is employed to estimate the system states under noisy conditions. The results demonstrate that NNAC provides robust and adaptive control of actuators, achieving efficient trajectory tracking via the transportation systems despite the influence of sensor and process noise disturbances. NNAC predominance was also observed in comparison with the conventional PID controller. Among the transportation configurations under the NNAC strategy, the RCTS exhibited the highest tracking accuracy with the lowest power consumption by the actuators. The power consumption of actuators installed on the LFFCTS was marginally higher than that of the RCTS. However, the translational motion accuracy of the follower vehicle in the LFFCTS was the lowest due to indirect actuation control through the formation controller. In contrast, actuators in the FCTS showed the highest power consumption while motion accuracy was comparatively lowest, attributed to the increased complexity of its dynamic positioning requirements. Full article

Background and Objectives: Cardiovascular disorders contribute substantially to medical ineligibility for compulsory military service in Lithuania. This study aimed to describe cardiovascular disease patterns and assess their association with military service eligibility among conscription-age individuals treated at a tertiary care center, considering gender, place of residence, age, and the most common cardiovascular causes of medical ineligibility. Materials and Methods: This retrospective hospital-based study included men and women aged 18–26 years with cardiovascular disease diagnoses defined according to ICD-10 codes specified by the Order of the Minister of National Defense of the Republic of Lithuania. Anonymized medical records from the Hospital of the Lithuanian University of Health Sciences Kaunas Clinics were reviewed. Participants were categorized into four military service eligibility classes based on medical history data and were stratified by gender, age, and place of residence. Results: The study included 521 participants (56.6% male, 43.4% female). Gender and residence showed no significant impact on military service eligibility. Younger individuals, particularly those aged 18–19, were more often deemed eligible, while eligibility declined with age. Males more commonly had essential hypertension and hypertensive heart disease, whereas females more frequently presented with paroxysmal tachycardia and other arrhythmias. Hypertension and other severe cardiovascular conditions most strongly reduced eligibility for compulsory military service, whereas rhythm disorders were more often compatible with service. Conclusions: In this hospital-based cohort of conscription-age individuals with cardiovascular disease, gender and place of residence did not significantly influence eligibility for military service. Eligibility declined with increasing age, and hypertension-related cardiovascular disorders were the leading cause of ineligibility among conscripts. Full article

The extensive application of unmanned aerial vehicles (UAVs) in power inspection, military operations and environmental monitoring demands stronger robustness and adaptability for autonomous decision-making systems. Existing methods suffer from heavy map dependence, high computational complexity and insufficient exploration and generalization. Traditional approaches based on expert rules and planning algorithms only suit fixed scenarios and degrade severely in complex dynamic environments. To address these problems, this paper proposes a complex-scene-oriented autonomous decision-making method for UAVs (CADU). It builds a closed-loop decision chain by integrating perception, strategy and execution modules, and adopts curiosity mechanism and contrastive learning to enhance exploration and adaptability. Experimental results show that the proposed CADU achieves an average reward of 0.85, a trajectory smoothness of 0.87, a flight stability of 0.85, and a cumulative collision count of $8\pm 1.2$, which significantly outperforms DDPG, PPO and SAC baselines. It provides a reliable and efficient scheme for UAV autonomous decision-making in complex scenarios. Full article

As ocean resources are further developed and utilized, bionic covert underwater acoustic communication (CUAC) is increasingly important for military and underwater telemetry applications. The primary purpose of this study was to design a highly secure and undetectable text information (TI) encryption mechanism to realize CUAC using real bottlenose dolphin vocalizations (BDVs). For this purpose, a chaotic encryption scheme, spread spectrum (SS) technology, and a modified chaotic Hénon map (MCHM) were integrated into a TI encryption and hiding (EH) mechanism. Four BDVs and four test TIs were employed to demonstrate the performance of the proposed MCHM-based TI EH mechanism (MCHMTIEHM). The simulation results show that the MCHMTIEHM yields more accurate de-hiding and decryption results. When the correct encryption and decryption parameters were used, the test TI was completely recovered and could be recognized by humans. When the MCHM encryption and decryption parameters ${SP}_x \mathrm{a}\mathrm{n}\mathrm{d} n_I$ were not identical, tests involving TI01, TI02, TI03, and TI04 demonstrated correct de-hiding and error decryption performance; in particular, the test TI had superior correct de-hiding and error decryption results, was unrecoverable, and could not be recognized by the human eye. The modified amplitude correlation coefficient (ACC) and modified unified average amplitude change intensity (UACI) metrics were used to evaluate the hiding performance of MCHM-based encryption of TI using BDVs. The simulation results show that the average modified ACC and average UACI were 0.99995924 and $3.84\times {10}^{-6}$, respectively. Performance was evaluated in terms of the average number of changing SS bit rates (NCSSBRs), the average number of changing bit rates (NCBRs), and the average number of changing character rates (NCCRs) for correct de-hiding and correct/erroneous TI decryption. The average NCSSBRs, NCBRs, and NCCRs were all 0% in correct de-hiding and decryption scenarios, while they were 49.29%, 47.65%, and 98.10%, respectively. with correct de-hiding and error-decryption scenarios. In summary, the proposed MCHMTIEHM yields superior encryption and hiding performance. Full article