Search Results (188)

Background: Psychological adaptation in high-demand contexts such as the military depends on the interaction of multiple emotional and psychological resources. Previous research has mainly examined emotional intelligence, resilience, and self-esteem using latent variable approaches, limiting understanding of how these variables dynamically interact within a broader network of interacting psychological resources. Objective: The present study aimed to analyze the network of relationships among emotional intelligence dimensions, resilience, and self-esteem, identifying the most central variables, their degree of clustering, and the strength of their associations. Methods: A cross-sectional study was conducted with 739 Spanish military personnel (M = 33.29; SD = 7.48). A regularized partial correlation network was estimated using the EBICglasso method (γ = 0.5). Centrality indices (strength and expected influence), clustering coefficients, and node predictability were analyzed. Network accuracy and stability were assessed through bootstrap procedures. Results: The estimated network showed moderate connectivity, indicating meaningful interrelations among emotional intelligence dimensions, resilience, and self-esteem. General mood and adaptability emerged as the most central nodes within the network. Resilience showed strong positive associations with adaptability and general mood, whereas self-esteem occupied a more peripheral position. Clustering analyses revealed a cohesive organization among adaptive emotional resources. Conclusions: Emotional intelligence dimensions and related psychological resources can be conceptualized as a dynamically interacting system associated with emotional adaptation in military personnel. The identification of central components may contribute to the development of targeted interventions aimed at strengthening emotional regulation and psychological adaptation in high-demand environments. Full article

The environmentally sustainable decontamination of chemical warfare agents (CWAs) remains a critical challenge. This study reports the solar-driven photocatalytic degradation of live CWAs—GD, HD, HN1, and HN2—using titanium dioxide (TiO₂) under natural sunlight. Experiments were conducted in an OPCW-designated laboratory to ensure authenticity and practical relevance. TiO₂ exhibited substantial photocatalytic activity, achieving 60% degradation of GD, 63% of HD, 76% of HN1, and 93% of HN2 after 6 h. High-resolution mass spectrometry (HR-MS) analysis suggested plausible degradation pathways for nitrogen mustards consistent with the higher apparent reactivity of HN2; detailed identification of intermediates and reactive oxygen species remains a subject for future investigation. These findings provide mechanistic insights into the photocatalytic behavior of nitrogen-based agents and address a notable gap in studies that have largely focused on sulfur mustards and nerve agents. Beyond military applications, this solar-assisted photocatalytic approach provides mechanistic information relevant to the green remediation of highly toxic organic contaminants and broader chemical hazard mitigation. This work contributes foundational knowledge toward eco-friendly decontamination technologies capable of mitigating diverse CWA threats. 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

The gradual increase in man-made objects in the space surrounding our planet is becoming increasingly evident. This significant rise in terrestrial materials is reflected in a greater presence of artificial satellites, space debris and waste from space missions. These objects orbiting close to Earth pose a significant risk in the event of uncontrolled re-entry, as well as collisions between the artificial satellites themselves, launch vehicles and space stations in Earth orbit. This article presents the experimental progress achieved during the prototype phase of a new model of robotic satellite observatory (SRO), featuring significant advances in its design and capabilities. These new SROs are intended to have dual capability to operate simultaneously in both scientific and military contexts. The possibility of forming a network with these devices will provide a system that substantially improves orbital determination and the identification of space objects of interest. The result presented here is an advanced model of the SRO, featuring substantial design improvements from both an ergonomic and economic perspective, as well as a significant enhancement in its ability to monitor and track space objects of uncertain origin that may be of interest or considered a threat to security, thereby expanding its Space Situational Awareness (SSA). Full article

Underwater cultural heritage represents a fragile and largely unexplored component of historical landscapes, particularly in dynamic fluvial and coastal environments. Despite increasing international attention to its protection, the spatial identification of submerged heritage remains methodologically challenging. This study proposes a geo-historical approach that integrates historical cartography and Geographic Information Systems (GIS) to identify areas of high archaeological potential in underwater contexts. Focusing on the Douro River in Porto (Portugal), a UNESCO World Heritage city with a long maritime and fluvial history, the research analyses a set of key historical maps from the eighteenth and nineteenth centuries, complemented by documentary and archaeological sources. These cartographic materials were georeferenced and critically assessed in QGIS, enabling the digitisation of features associated with land–water interaction, navigation hazards, port infrastructures, and military defences. The resulting spatial dataset was used to generate an interpretative map and a kernel density model highlighting potential underwater heritage hotspots along the riverbed and riverbanks. The findings identify several priority zones, including the river mouth, historic quays, former shipbuilding areas, and sectors linked to nineteenth-century defensive structures. While the study does not include in situ verification, it demonstrates the value of historical maps as predictive tools for guiding targeted underwater surveys and proposes a transferable, cost-effective framework for heritage prospection and management in historically active fluvial–estuarine settings. Full article

The primary objective of this research is to identify, analyse, and compare the development of risk management approaches adopted by Slovak industrial enterprises in two distinct economic periods: during a phase of economic growth (2019) and during a period of global crises and regional crises with significant global implications, which have had substantial global economic, energy, and security impacts, as well as the increasing intensity of cyber threats affecting enterprises in Slovakia (2022–2023). Emphasis is placed on identifying key factors influencing the effectiveness of risk management implementation, as well as on assessing the use of individual stages of the risk management process in business practice. The research has a quantitative character and consists of two empirical surveys conducted through questionnaire-based data collection. The first survey was carried out in 2019 under conditions of economic growth, while the second was conducted in 2022–2023 in the context of multiple global crises and regional crises, particularly the impacts of the COVID-19 pandemic, the global energy crisis, the military conflict in Ukraine, and increasing cyber threats. The first study obtained 450 valid responses, and the second obtained 390 responses from enterprises operating across various sectors of the private economy in Slovakia. The results of the study confirmed the existence of significant differences in companies’ approaches to risk management depending on the economic context. During the period of economic growth, the main reason for insufficient attention to risks was low staff motivation, with enterprises focusing primarily on risk identification, analysis, and assessment, and less on designing specific mitigation measures. In contrast, during the period of global crises and regional crises, companies’ attitudes shifted, with stronger resistance to implemented measures but, at the same time, increased attention to the development of risk-reduction actions. Neglecting systematic preventive steps increases companies’ vulnerability to crises, which may result in operational, financial, and reputational losses, delayed responses, and a decline in competitiveness. The two-phase nature of the research made it possible to capture the dynamics of managerial behaviour under different economic conditions and to formulate practical recommendations for integrating risk management into both strategic and operational levels of management. Full article

This paper presents the concept and implementation of the BIM–CARVER tool, which integrates the CARVER matrix (Criticality, Accessibility, Recuperability, Vulnerability, Effect, Recognizability) with an open BIM environment based on the IFC standard. Originally developed by the US military for target analysis, the CARVER matrix has evolved into a defensive tool for protecting critical infrastructure. Traditionally, physical security assessments of buildings are performed manually, separately from the digital model, contradicting the principles of Security by Design, which assume that security aspects should be taken into account at the early stages of design. As part of research conducted in accordance with the Design Science Research methodology, a plugin for the Bonsai platform (BlenderBIM) was developed, enabling the assignment of vulnerability assessments to individual elements of the IFC model according to six CARVER criteria on a scale of 1–10, the visualization of results directly in the modeling environment, and the generation of security reports in HTML format. The tool was validated on a set of ten building models of varying purpose and complexity. The results confirmed the effectiveness of the tool in systematically identifying and classifying building elements into four risk categories: critical, important, significant, and insignificant. The developed semi-automated solution supports designers and security specialists in the proactive identification of threats and enables the comparison of design variants in terms of the aggregated risk level, contributing to the implementation of Security by Design principles in design practice. By reducing the need for costly security retrofits and enabling resource-efficient design decisions, the proposed approach also contributes to the sustainability objectives in the built environment. Full article

(1) Background: The families of military personnel preparing for deployment are exposed to anticipatory stressors that may adversely affect their psychological well-being. This study aimed to examine the associations between anxiety, psychological resilience, and trait hope among the family members of military personnel during the pre-deployment period. (2) Methods: A cross-sectional online survey was conducted between 20 September and 20 December 2025 and included 73 Romanian adult participants. From a psychiatric perspective, anxiety during the pre-deployment phase represents a clinically relevant form of anticipatory distress that may benefit from early identification and preventive intervention. Anxiety was assessed using the Generalized Anxiety Disorder 7-item scale (GAD-7), psychological resilience with the Brief Resilience Scale (BRS), and trait hope with the Adult Hope Scale (Agency and Pathways subscales). Pearson correlation analyses, multivariable linear regression, and hierarchical regression models were applied. (3) Results: Participants reported moderate anxiety levels (GAD-7 mean 7.52 ± 4.98). Anxiety was strongly negatively correlated with psychological resilience (r = −0.75, p < 0.001) and moderately negatively correlated with total hope (r = −0.67, p < 0.001), Agency (r = −0.61, p < 0.001), and Pathways (r = −0.64, p < 0.001). Psychological resilience emerged as a significant negative predictor of anxiety (β = −0.64, p < 0.001). Hierarchical regression showed that trait hope explained additional variance in anxiety severity beyond resilience and sociodemographic variables (ΔR² = 0.07, p < 0.001). (4) Conclusions: Psychological resilience and trait hope were independently and jointly associated with lower pre-deployment anxiety in military families, underscoring their relevance to preventive and community psychiatry as modifiable resources for early screening and non-pharmacological intervention. Full article

Background/Objectives: To identify US military unknowns, the Armed Forces Medical Examiner System’s Armed Forces DNA Identification Laboratory has historically relied upon mitochondrial DNA and Y-chromosomal short tandem repeat testing. Where no appropriate family reference sample (FRS) is available or skeletal samples are degraded, autosomal single nucleotide polymorphism (SNP) testing with next-generation sequencing could assist. Methods: A method utilizing hybridization capture enrichment of a 95,000 (95K) SNP panel, amenable to FRS and extremely challenging samples, was validated. The Parabon Fx Forensic Analysis Platform was used for analysis and extended kinship inference. Skeletal samples (n = 65) and associated FRS (n = 64) were selected for a performance evaluation and case-type sample study. Results: Considering FRS with ≥7 ng DNA input into library preparation, 94% yielded ≥66,320 SNPs at ≥5X coverage. SNP recovery for skeletal samples at ≥1X coverage ranged from 5 to 94,197 SNPs, averaging 40,770 SNPs. When skeletal samples resulted in ≥13,000 SNPs, the most likely relationship category was consistent with the expected relationship. A log10 likelihood ratio of ≥4 and a posterior probability of ≥99.99% were established as thresholds for strong statistical support, and 87% of inferences met these thresholds while 13% were considered inconclusive. Pairwise kinship inference between unrelated individuals yielded an unrelated result in 85% of comparisons, 66% with strong statistical support. There were 170 instances of false positive 4th degree relationship inferences with strong statistical support. All false positives involved skeletal samples from individuals of admixed ancestry. Conclusions: With this approach, autosomal SNP testing can result in reliable kinship inferences between related individuals out to 3rd, and in some cases 4th, degree relationships, increasing the scope of eligible FRS to aid in identifications. Full article

Background: Early diagnosis and identification of causative pathogens using blood culture in patients suspected of blood stream infection (BSI) and sepsis are critical for improving patient outcomes through earlier and more targeted treatment. There is a need for tools that can guide the use of microbiologic diagnostics, especially where resources are limited, such as in lower- and middle-income countries (LMICs), pandemic and mass-casualty scenarios, and prolonged field care settings during military operations. Methods: Post hoc retrospective analysis of individual patient data from three prospective clinical studies, conducted in North America, Europe and Africa, to investigate the association between SeptiCyte RAPID test results (SeptiScores) and blood culture (BC) results. Hypothesis: that a significant correlation exists between elevated SeptiScores and positive blood culture results, and between low SeptiScores and negative blood culture results. Results: The area under the receiver operating characteristic curve (ROC AUC) was 0.91 for 85 BC(+) versus 257 SIRS and was 0.80 for 164 BC(−) versus 257 SIRS. As the SeptiScore increases, the relative probability of a septic patient being BC(+) as opposed to BC(−) also increases. A non-linear positive correlation is observed. Below a crossover point at SeptiScore 10, the ratio of probabilities of BC(+) sepsis/BC(−) sepsis is <1, while above the crossover point, this ratio is >1. Thus, septic patients with SeptiScores >10 have a higher probability of being BC(+) compared to BC(−). Conclusions: Elevated SeptiScores, obtained before blood culture results, are indicative of increased blood culture positivity. This may have clinical utility, particularly in resource-limited settings, as an aid for improving the efficiency of blood culture practice, for instance, by informing patient selection and interpretation of blood culture results. Full article

Multispectral infrared detection plays a crucial role in advanced applications spanning environmental monitoring, military surveillance, and biomedical diagnostics, offering superior target identification accuracy compared to single-band imaging techniques. In this work, we synthesized four distinct bands of colloidal quantum dots (CQDs)—specifically, a cut-off of 1.3 µm with PbS CQDs and 1.8 µm, 2.6 µm, and 3.5 µm with HgTe CQDs—and employed them to construct planar multiband infrared photodetectors. The device exhibited a clear photoresponse at room temperature from 0.8 µm to 3.5 µm, with responsivity of 5.39 A/W and specific detectivity of 2.01 × 10¹¹ Jones at 1.8 µm. This materials–device co-design strategy integrates wavelength-selective CQD synthesis with planar pixel-level patterning, providing a versatile pathway for developing low-cost, solution-processed, multiband infrared photodetectors. Full article

This study aims to examine the methodological applicability of the Theory of Inventive Problem Solving (TRIZ) in the conservation and revitalization of traditional military settlements. Using Zhenjing Village in Jingbian County as a case, the research constructs a systematic framework for contradiction identification and strategy generation. Methods: Through preliminary surveys, data integration, and system modeling, the study identifies major conflicts among authenticity preservation, ecological carrying capacity, and community vitality in Zhenjing Village. Technical contradiction matrices, separation principles, and the Algorithm of Inventive Problem Solving (ARIZ) are employed for structured analysis. Further, system dynamics modeling is used to simulate the effectiveness of strategies and to evaluate the dynamic impacts of various conservation interventions on authenticity maintenance, ecological stress, and community vitality. The research identifies three categories of core technical contradictions and translates the 39 engineering parameters into an indicator system adapted to the cultural heritage conservation context. ARIZ is used to derive the Ideal Final Result (IFR) for Zhenjing Village, which includes self-maintaining authenticity, self-regulating ecology, and self-activating community development, forming a systematic strategy. System dynamics simulations indicate that, compared with “inertial development,” TRIZ-oriented strategies reduce the decline in heritage authenticity by approximately 40%, keep ecological pressure indices below threshold levels, and significantly enhance the sustainability of community vitality. TRIZ enables a shift in the conservation of traditional military settlements from experience-driven approaches toward systematic problem solving. It strengthens conflict-identification capacity and improves the logical rigor of strategy generation, providing a structured and scalable innovative method for heritage conservation in arid and ecologically fragile regions in northern China and similar contexts worldwide. Full article

With the increasing adoption of advanced drone technologies across diverse fields, the Internet of Drones (IoD) has emerged as a novel mobility paradigm, particularly enhancing Intelligent Transportation Systems (ITS) in urban environments. Despite its significant potential, the IoD faces substantial challenges due to inherent resource constraints such as limited computational power and energy capacity, which hinder the implementation of robust cybersecurity solutions. These limitations expose IoD networks to various security vulnerabilities and privacy threats, necessitating an exhaustive analysis and understanding of these risks. In this paper we introduce SafeIoD, a comprehensive security framework designed to establish standardized procedures for proactive risk identification in Internet of Drones (IoD) devices. It involves sequential steps to determine the trustworthiness of devices subjected to these certification. Therefore, SafeIoD seeks to ensure a basic security level before implementation in a real scenario, where the network devices are evaluated in regards to the specific security requirements. Validation through experimental testing with 15 participants across four IoD deployment scenarios and one military certification case demonstrated the framework’s effectiveness: the tool achieved 73% user satisfaction rating, successfully identified an average of 3.0 security requirements per device, and provided specific lightweight cryptographic algorithm recommendations for 62.2% of elicited requirements. In a tactical military scenario simulation, the framework accurately predicted risk propagation patterns, with COOJA network simulations confirming that implementation of framework-recommended protocols reduced successful attack propagation from 60% to below 5% of the network. Full article

With the extensive application of unmanned aerial vehicles (UAVs) in both military and civilian domains, the significance of UAV identification technology has become increasingly prominent. Among various recognition methods, voice recognition has garnered considerable attention due to its advantages of low cost and easy deployment. However, most existing research primarily focuses on isolating UAV sounds from noise signals in complex environments, with limited studies on long-distance UAV sound recognition. Based on this, this paper proposes a frequency domain feature extraction method based on harmonic features. By analyzing the harmonic features of UAV sounds, we select stable parameters with strong robustness against interference capabilities as the main features to minimize information redundancy and feature fluctuation. The experimental results indicate that this method achieves a recognition accuracy of 78.03% for the DJI Phantom 4 Pro V2.0 UAV at a distance of 120 m. To validate the proposed method, comprehensive comparisons against traditional MFCC, Log-Mel Spectrogram, and modern Raw Waveform CNN (M5) baselines demonstrate the superior robustness of the proposed approach. While these comparative methods exhibited significant performance drops in challenging long-distance scenarios (e.g., accuracies falling below 24% for the DJI Mavic Pro), the proposed method maintained consistent identification capabilities, validating its effectiveness in low-signal environments. Full article

Affective Computing and emotion recognition hold significant importance in healthcare, identity verification, human–computer interaction, and related fields. Accurate identification of emotion is crucial for applications in medicine, education, psychology, and military domains. Electroencephalographic (EEG) signals have gained widespread application in emotion recognition due to their inherent characteristics of being non-concealable and directly reflecting brain activity. In recent years, with the establishment of open datasets and advancements in deep learning, an increasing number of researchers have integrated EEG with deep learning methods for emotion recognition studies. This review summarizes commonly used deep learning models in EEG-based emotion recognition along with their applications in this field, including the design of different network architectures, optimization strategies, and model designs based on EEG signal features. We also discuss limitations from the perspectives of commonality–individuality (C-I) and suggest improvements. The review outlines future research directions and provided a minimal C-I framework to assess models. Through this review, we aim to provide researchers in this field with a comprehensive reference and approach to balance universality and personalization to promote the development of deep learning-based EEG emotion recognition methods. Full article