Search Results (1,598)

A considerable number of people are exposed to noise from military aircraft daily, but its health effects have not been sufficiently examined. This study assessed the association of exposure to such noise with mental well-being and sleep disturbance among people living in Okinawa prefecture, where there are two U.S. military air bases. In 2024, data were collected from 394 residents in high-, low-, and no-exposure communities using the WHO-5 Well-being Index and the Athens Insomnia Scale. Among respondents, 55.8% were female; the largest age groups were 70’s (25.4%) and 60’s (23.6%). Poor mental well-being and sleep disturbance were most prevalent in the high-exposure community (poor mental well-being: 38.2%, sleep disturbance: 46.6%), followed by low-exposure (36.1%, 46.3%) and no-exposure (21.9%, 29.0%) communities. Multivariate logistic regression analyses showed that compared to no-exposure community, the high-exposure and low-exposure communities were significantly more likely to have poor mental well-being (odds ratio (OR): 1.84, 95% confidence interval (CI): 1.05–3.23; OR: 1.94, 95% CI: 1.05–3.56), as well as sleep disturbance (OR: 1.98, 95% CI: 1.17–3.35; OR: 2.04; 95% CI: 1.16–3.59, respectively). The results suggest that there is a substantial need to address the noise from military aircraft in Okinawa. 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

Background: Anatomopathological reports are largely unstructured, which limits automated data extraction, interoperability, and large-scale research. Manual extraction and standardization are costly and difficult to scale. Objective: We developed and evaluated an automated pipeline for entity extraction and multi-ontology normalization of anatomopathological reports. Methods: A corpus of 560 reports from the Military Hospital of Tunis, Tunisia, was manually annotated for three entity types: sample type, test performed, and finding. The entity extraction utilized BioBERT v1.1, while the normalization combined BioClinicalBERT multi-label classification with retrieval-augmented generation, incorporating both dense and BM25 sparse retrieval over SNOMED CT, LOINC, and ICD-11. The performance was measured using precision, recall, F1-score, and statistical tests. Results: BioBERT achieved high extraction performance (F1: 0.97 for the sample type, 0.98 for the test performed, and 0.93 for the finding; overall 0.963, 95% CI: 0.933–0.982), with low absolute errors. For terminology mapping, the combination of BioClinicalBERT and dense retrieval outperformed the standalone and BM25-based approaches (macro-F1: 0.6159 for SNOMED CT, 0.9294 for LOINC, and 0.7201 for ICD-11). Cohen’s Kappa ranged from 0.7829 to 0.9773, indicating substantial to near-perfect agreement. Conclusions: The pipeline provides robust automated extraction and multi-ontology coding of anatomopathological entities, supporting transformer-based named entity recognition with retrieval-augmented generation. However, given the limitations of this study, multi-institutional validation is needed before clinical deployment. Full article

Heterogeneous remote sensing image change detection (Hete-CD) holds significant research value in military and civilian fields. The existing methods often rely on expert experience to design fixed deep network architectures for cross-modal feature alignment and fusion purposes. However, when faced with diverse land cover types, these methods often lead to blurred change boundaries and structural distortions, resulting in significant performance degradations. To address this, we propose an adaptive adversarial learning-based heterogeneous remote sensing image change detection method based on the differentiable filter combination search (DFCS) strategy to provide enhanced generalizability and dynamic learning capabilities for diverse scenarios. First, a fully reconfigurable self-learning discriminator is designed to dynamically synthesize the optimal convolutional architecture from a library of atomic filters containing basic operators. This provides highly adaptive adversarial supervision to the generator, enabling joint dynamic learning between the generator and discriminator. To further mitigate modality differences in the input stage, we integrate a feature fusion module based on the Gabor and local normalized cross-correlation (G-LNCC) to extract modality-invariant texture and structure features. Finally, a geometric structure-based collaborative supervision (GSCS) loss function is constructed to impose fine-grained constraints on the change map from the perspectives of regions, boundaries, and structures, thereby enforcing physical properties. Comparative experimental results obtained on five public Hete-CD datasets show that our method achieves the best F1 values and overall accuracy levels, especially on the Gloucester I and Gloucester II datasets, achieving F1 scores of 93.7% and 95.0%, respectively, demonstrating the strong generalizability of our method in complex scenarios. Full article

Hyperspectral image (HSI) target detection plays a critical role in both military and civilian applications, including military reconnaissance, environmental monitoring, and precision agriculture. However, the complex background of the scene severely restricts the further improvement of hyperspectral target detection performance. To address this challenge, we propose a diffusion model hyperspectral target detection method based on multivariate Gaussian background noise. The method constructs multivariate Gaussian-distributed background noise samples and introduces them into the forward diffusion process of the diffusion model. Subsequently, the denoising network is trained, the conditional probability distribution is parameterised, and a designed loss function is used to optimise the denoising performance and achieve effective suppression of the background, thus improving the detection performance. Moreover, in order to obtain accurate background noise, we propose a background noise extraction strategy based on spatial–spectral centre weighting. This strategy combines with the superpixel segmentation technique to effectively fuse the local spatial neighbourhood information of HSI. Experiments conducted on four publicly available HSI datasets demonstrate that the proposed method achieves state-of-the-art background suppression and competitive detection performance. The evaluation using ROC curves and AUC-family metrics demonstrates the effectiveness of the proposed background-suppression-guided diffusion framework. Full article

Exercise fatigue is a critical factor that compromises athletic performance, increases the risk of musculoskeletal injury, and threatens safety in military and occupational settings. Reliable monitoring of fatigue is therefore essential for optimizing training, preventing injury, and safeguarding long-term health. Biomechanical indicators, including joint kinematics, ground reaction forces, and electromyographic signals, provide valuable insight into the biomechanical manifestations of fatigue. Although traditional laboratory-based methods are accurate, they are costly, cumbersome, and unsuitable for continuous field monitoring. Recent advances in wearable technologies, particularly inertial measurement units (IMUs), insole pressure sensors (IPSs), and surface electromyography (sEMG), enable continuous, noninvasive, and real-time assessment of biomechanical changes during exercise fatigue. This review synthesizes current progress in IMU-, IPS-, and sEMG-based wearable systems for biomechanical exercise fatigue monitoring, highlighting their principles, strengths, and challenges. Full article

Background: Shoulder instability imposes a substantial burden in U.S. military populations, yet epidemiology and outcomes reporting is heterogeneous. This study aims to quantify the epidemiology of shoulder instability among U.S. active-duty servicemembers and to report operative management patterns and outcomes. Methods: A systematic review was performed by searching MEDLINE, EMBASE, Scopus, Cochrane, and SPORTDiscus through 1 August 2025. Eligible studies enrolled U.S. active-duty servicemembers with clinical and/or radiographic evidence of instability. After a single comprehensive search with uniform inclusion criteria, studies were assigned to two prespecified cohorts: (1) epidemiology (incidence, directionality, risk factors) and (2) operative management/outcomes (procedure distribution, failure, complications, return to duty [RTD] and return to sport [RTS]). Incidence was pooled as a person-years–weighted fixed-effect estimate; directionality proportions were meta-analyzed with random-effects (logit-transformed) models among patient-level, unidirectional cases. Results: Forty-nine studies were included (epidemiology, n = 8; outcomes, n = 41). Three epidemiologic datasets (42,310 events; 20,472,363 person-years) yielded a pooled military incidence of 2.07 per 1000 person-years (95% CI, 2.05–2.09). Among unidirectional cases (n = 916 shoulders), anterior instability comprised 83.9% (95% CI, 70.5–91.9) and posterior the remaining 16.1% (95% CI, 8.1–29.5). Outcome series most commonly reported arthroscopic Bankart repair (n = 933 shoulders), bony augmentation (e.g., Latarjet/Bristow; n = 700), posterior labral repair (n = 649), combined repairs (n = 511), and open Bankart (n = 442). Weighted mean failure ranged 4.7–23.6%; complications 5.2–10.9%; and reoperations 5.3–17.7%. RTD ranged 50.0–84.7% and RTS 4.8–75.0%. Conclusions: Shoulder instability in U.S. servicemembers occurs at rates exceeding population-based civilian estimates, with a relatively greater share of posterior and combined patterns. Operative outcomes vary substantially across procedures. Full article

Unmanned aerial vehicle (UAV) networks have become an essential component of modern civilian and military infrastructures. However, the communication channels between UAVs and their control entities remain vulnerable to spoofing and message tampering attacks. Although conventional digital signature schemes can ensure message authentication and integrity, they often undermine the real-time responsiveness of UAV operations and fail to protect the privacy of signers. To address these limitations, we propose an expressive attribute-based proxy signature (EABPS) scheme tailored for UAV networks. The scheme enables fine-grained authorization and authentication, ensuring that only entities whose attributes satisfy a specified access structure can generate valid proxy signatures. Furthermore, the scheme preserves signer privacy by decoupling signatures from explicit identities. Comprehensive security analysis and extensive experimental evaluation demonstrate that the proposed EABPS scheme achieves strong security guarantees while offering improved computational efficiency and expressiveness, making it a practical solution for secure communication in UAV networks. Full article

Background/Objectives: Additional load is associated with a significant increase in energy expenditure during soldiers’ movement. The level of energy expenditure during military tasks depends on the speed at which soldiers move. The aim of the study was to determine how the speed of movement and military load in the form of a 20-kg backpack affect the energy expenditure of special forces operators. Methods: The study included a group of 24 special forces operators. The energy expenditure of participants was measured using a portable Cosmed K5 gas analyzer operating in “breath-by-breath” mode. Energy expenditure was calculated based on VO₂ and VCO₂ data. Respiratory exchange ratio (RER) was recorded in parallel with VO₂ and VCO₂ and used to calculate the oxidation of energy substrates. The soldiers moved in 6-min intervals at the following speeds: 4.5 km/h, 6.5 km/h, 8.5 km/h, 10.5 km/h. First, the soldiers covered each speed without a load, and then with a 20 kg tactical military backpack. Results: The analysis showed that increasing speed and the use of an external load significantly increased all physiological and metabolic responses. Speed × load interactions were observed for some metabolic variables, whereas no such interactions were found for heart rate. Conclusions: Adding a 20 kg tactical backpack causes a significant increase in energy expenditure at all speed levels. The additional load resulted in an average increase of 10% in heart rate (%HRmax) and 20% in oxygen uptake (%VO₂max). A more than threefold increase in energy expenditure was recorded (14.77 kcal/min without load and 17.70 kcal/min with a backpack at a speed of 10.5 km/h vs. 5.01 kcal/min and 6.35 kcal/min at a speed of 4.5 km/h). Full article

In military operational environments, satellite navigation is typically unavailable, and unmanned aerial vehicle swarms must rely on inter-node communication links for distance measurement to achieve relative positioning. When the absolute positions of a subset of nodes are known, this approach can also enable absolute positioning of the entire swarm. Conventional relative positioning methods often overlook the inherent difference in ranging ability, assuming the same distance errors across all nodes. This oversimplification leads to significant positioning inaccuracies and slow convergence. To address these limitations, this paper proposes a distance error variance adjustable relative positioning method, DEVARP. The proposed method assigns varying confidence weights to inter-node distance measurements based on link quality, and solves it by continuous numerical iteration so as to achieve high-speed and high-precision positioning. In the simulation experiments involving a 32-node UAV swarm, the results show that the confidence-adjustable method achieves an absolute positioning error below 1 m with a probability of 91.05%, compared to 76.57% for traditional methods—a 14.48% improvement. Additionally, the proposed method converges within seven iterations with a probability of 94.10%. In contrast, conventional methods achieve the same result in seven steps with a probability of 78.83% —a 17.53% performance gap—which confirms both the accuracy and computational efficiency of the method. Full article

Open-winding permanent-magnet synchronous motors feature flexible control and a high fault-tolerance capability, making them widely used in high-reliability and high-power scenarios such as military equipment and electric locomotives. To address the issues that traditional model predictive control fails to balance, such as zero-sequence current suppression, system loss optimization and the reliance of weight parameter design on experience (with online optimization consuming excessive resources), this paper proposes an OW-PMSM MPC strategy for loss optimization and a weight design method based on a residual neural network. Specifically, the former strategy adds a zero-sequence current suppression term and a loss quantification term to the MPC cost function, enabling coordinated control of the two objectives; the latter establishes a mapping between weight parameters and motor performance via ResNet (which avoids the gradient vanishing problem in deep networks) and outputs optimal weight parameters offline to save online computing resources. Comparative experiments under two operating conditions show that the improved MPC strategy reduces system loss by 25%, while the ResNet-based weight design improves the performance of the drive system by 30%, fully verifying the effectiveness of the proposed methods. Full article

Because of the advancement of IT, cross-domain environments have emerged where independent clouds with different security policies share data. However, sharing data between clouds with heterogeneous security levels is a challenging task, and most existing access control schemes focus on a single cloud domain. Among various access control models, RBAC is suitable for cross-domain data sharing, but existing RBAC schemes cannot provide strong role privacy and do not support freshness in role verification, so they are vulnerable to replay-based misuse of credentials. In this paper, we propose an RBAC scheme for cross-domain cloud environments based on a hash-chain-augmented zk-SNARK and identity-based signatures. The TA issues IBS-based role signing keys to users, and the user proves, through a zk-SNARK circuit, that there exists a valid role signing key satisfying the access policy without revealing the concrete role information to the CDS. In addition, a synchronized hash chain between the user and the CDS is embedded into the proof so that each proof is tied to the current hash-chain state and any previously used proof fails verification when replayed. We formalize role privacy, replay resistance, and MitM resistance in the cross-domain setting and analyze the proposed scheme by comparing it with Saxena and Alam’s I-RBAC, Xu et al.’s RBAC, MO-RBE, and PE-RBAC. The security analysis shows that the proposed scheme achieves robust role privacy against both the CDS and external attackers and prevents replay and man-in-the-middle attacks. Furthermore, the computational cost evaluation based on the number of pairing, exponentiation, point addition, and hash operations confirms that the verifier-side overhead remains comparable to existing schemes, while the additional prover cost is the price for achieving stronger privacy and security. Therefore, the proposed scheme can be applied to cross-domain cloud systems that require secure and privacy-preserving role verification, such as military, healthcare, and government cloud infrastructures. Full article

In wireless communication, information security, and anti-interference technology, modulation recognition of frequency-hopping signals has always been a key technique. Its widespread application in satellite communications, military communications, and drone communications holds broad prospects. Traditional modulation recognition techniques often rely on expert experience to construct likelihood functions or manually extract relevant features, involving cumbersome steps and low efficiency. In contrast, deep learning-based modulation recognition replaces manual feature extraction with an end-to-end feature extraction and recognition integrated architecture, where neural networks automatically extract signal features, significantly enhancing recognition efficiency. Current deep learning-based modulation recognition research primarily focuses on conventional fixed-frequency signals, leaving gaps in intelligent modulation recognition for frequency-hopping signals. This paper aims to summarise the current research progress in intelligent modulation recognition for frequency-hopping signals. It categorises intelligent modulation recognition for frequency-hopping signals into two mainstream approaches, analyses them in conjunction with the development of intelligent modulation recognition, and explores the close relationship between intelligent modulation recognition and parameter estimation for frequency-hopping signals. Finally, the paper summarises and outlines future research directions and challenges in the field of intelligent modulation recognition for frequency-hopping signals. Full article

Background: Post-traumatic stress disorder (PTSD) is a severe psychiatric condition prevalent among combat veterans. Its diagnosis is challenging due to the heterogeneity of clinical presentations and the complex interplay of pathogenic factors. Objective: This study aimed to develop and validate a diagnostic algorithm for combat-related PTSD by integrating clinical data with a panel of biological markers associated with blood–brain barrier disruption (anti-GFAP and anti-NSE antibodies), HPA axis dysfunction (cortisol), and neuroinflammation (IL-6, IL-8). Methods: A total of 721 male participants were enrolled: 434 veterans with PTSD (F43.1), 147 combat veterans without PTSD, and 140 non-combat military controls. All participants underwent clinical and psychometric assessment (Likert scale, HADS). Serum levels of biomarkers were measured using ELISA. Statistical analysis included non-parametric tests, correlation analysis, and binary logistic regression with Wald’s method to build a predictive model. Results: The binary logistic regression model identified cortisol and IL-6 as the most significant predictors of PTSD. The final algorithm, based on a cortisol level below 199.8 nmol/L and an IL-6 level above 0.002438 pg/mL, correctly classified 78% of patients (AUC = 0.724, 95% CI [0.669, 0.779]). Furthermore, levels of IL-4, IL-8, and cortisol positively correlated with the severity of combat stress factors, independent of physical injuries. Conclusions: We developed a novel diagnostic algorithm for combat-related PTSD based on cortisol and IL-6 levels, demonstrating high accuracy. The correlation between neuroinflammatory markers and the severity of combat exposure suggests their role as primary indicators of stress response, highlighting their utility for early risk identification and targeted interventions. Full article

Occupational demands, such as load carriage in tactical professions, do not discriminate based on sex. The aim of this study was to explore the differences in metabolic cost of a loaded pack march between the sexes in both absolute and relative terms. Twelve Army personnel (six males and six females) volunteered to complete three identical load carriage marches (5 km at 5.5 km/h, carrying 30 kg), across flat (on road) and undulating (gravelled path) terrain as part of a larger equipment trial. Heart rate (HR) response (HR average and maximum) was monitored with a Polar Team Pro unit and oxygen consumption with VO Master Pro (VO₂ average and maximum) with the level of significance set at 0.05. There were no significant differences in age, years of experience, absolute loads carried, or completion time for each of the three events. Male soldiers were significantly taller (182.3 ± 6.2 cm vs. 167.4 ± 6.9 cm), heavier (88.2 ± 8.7 kg vs. 70.9 ± 10.6 kg), carried significantly less relative load (34.3 ± 3.4% vs. 43.2 ± 7.5%), and had significantly greater predicted VO_2max (56.7 ± 6.1 mL/kg/min vs. 45.0 ± 2.9 mL/kg/min). A linear mixed model identified a significant main effect of sex on both average HR (β = −1.10) and peak HR (β = −1.27), and on average VO₂ (β = −0.68), but not peak VO₂. While the study was not powered to detect sex differences, the large effect sizes observed suggest meaningful physiological differences warranting further investigation. Female soldiers faced significantly greater metabolic costs when carrying the same loads and moving at the same speed and across the same terrain as their male counterparts. Adequate recovery and pacing strategies should be considered for these events, especially during training. Full article