Search Results (509)

This paper develops empirical relationships to estimate FAA/EASA and MIL-3013B rules-compliant take-off field performance for single and multi-engine aircraft. Recent experience with modern aircraft flight manuals revealed that popular empirical legacy methods are no longer accurate; improvements in tires and brakes lead to significantly shorter certified distances. This work relies upon a survey of current operational aircraft and extensive numerical simulations of generic configurations to support the development of a collection of new equations to estimate take-off performance for single and multi-engine aircraft under dry and wet conditions. These relationships are individually tailored for civilian and U.S. Military rules; they account for the superior capability of modern braking systems and the implications of minimum-control speed on the certified distance. Full article

With the widespread application of small quadcopter drones in the military and civilian fields, the security challenges they face are gradually becoming apparent. Especially in dynamic environments, the rapidly changing conditions make the flight of drones more complex. To address the computational limitations of small quadcopter drones and meet the demands of obstacle perception in dynamic environments, a LiDAR-based obstacle perception algorithm is proposed. First, accumulation, filtering, and clustering processes are carried out on the LiDAR point cloud data to complete the segmentation and extraction of point cloud obstacles. Then, an obstacle motion/static discrimination algorithm based on three-dimensional point motion attributes is developed to classify dynamic and static point clouds. Finally, oriented bounding box (OBB) detection is employed to simplify the representation of the spatial position and shape of dynamic point cloud obstacles, and motion estimation is achieved by tracking the OBB parameters using a Kalman filter. Simulation experiments demonstrate that this method can ensure a dynamic obstacle detection frequency of 10 Hz and successfully detect multiple dynamic obstacles in the environment. Full article

How leaders communicate with followers is a core element of leadership. However, research on leader communication techniques remains fragmented, limiting our understanding of their differential effects on follower cognition, affect, and behavior. To facilitate systematic research comparing the effects of different communication techniques, we propose a framework for leader communication techniques. We hypothesize that different communication techniques can be categorized into cognitive, affective, and behavioral leader communication techniques that influence cognitive, affective, and behavioral follower outcomes, respectively. To test this assumption, we developed and pre-tested video vignette material, which we then used in a between-subjects experiment in the military context. We assigned 368 military recruits to one of the three conditions (cognitive versus affective versus behavioral) to examine how these techniques relate to proximal and distal follower outcomes. We found few differences in the impact of cognitive, affective, and behavioral leader communication techniques on follower outcomes. The leader was perceived similarly, regardless of the communication techniques used. Only for specific affective follower outcomes (i.e., warmth and charisma of the leader), affective leader communication techniques were more effective than cognitive and behavioral techniques. We discuss implications for leader communication research, outline practical implications for leaders, and propose directions for future research. Full article

The research was initiated by the Plečnik House gallery (Ljubljana, Slovenia) and focuses on the 3D architectural reconstruction of the unrealised monument of the Czech military leader Jan Žižka, designed by the Slovenian architect Jože Plečnik. In addition, the experience with the 3D reconstructed monument in the exhibition “Plečnik and the Sacred” was analysed. Using the available references and interpretative approaches, a digital and 3D-printed reconstruction was created that retains Plečnik’s architectural style. The experimental phase included a detailed interpretation of the studied references, 3D modelling, 3D printing, exhibition and experience analysis. The dimensions of the finished 3D-printed model are 52.80 × 55.21 × 44.60 cm. It was produced using stereolithography (SLA) for figurative elements and fused deposition modelling (FDM) for architectural components. The reconstruction was evaluated using participant testing, including semantic differential analysis, comparative studies, and knowledge-based questionnaires. The results showed that architectural elements were reconstructed with an average similarity score of 1.97 out of 5. Statues followed with a score of 1.81, and props, though detailed, met audience expectations, scoring 1.61. Clothing received the lowest score of 1.40. This research emphasises the importance of a hypothetical digital 3D reconstruction of never constructed monument for broader understanding of Plečnik’s legacy. Full article

Due to its superior maneuverability and concealment, the micro flapping-wing aircraft has great application prospects in both military and civilian fields. However, the development and optimization of lightweight materials have always been the key factors limiting performance enhancement. This paper designs the flapping mechanism of a single-degree-of-freedom miniature flapping wing aircraft. In this study, T800 carbon fiber composite material was used as the frame material. Three typical wing membrane materials, namely polyethylene terephthalate (PET), polyimide (PI), and non-woven kite fabric, were selected for comparative analysis. Three flapping wing configurations with different stiffness were proposed. These wings adopted carbon fiber composite material frames. The wing membrane material is bonded to the frame through a coating. Inspired by bionics, a flapping wing that mimics the membrane vein structure of insect wings is designed. By changing the type of membrane material and the distribution of carbon fiber composite materials on the wing, the stiffness of the flapping wing can be controlled, thereby affecting the mechanical properties of the flapping wing aircraft. The modal analysis of the flapping-wing structure was conducted using the finite element analysis method, and the experimental prototype was fabricated by using 3D printing technology. To evaluate the influence of different wing membrane materials on lift performance, a high-precision force measurement experimental platform was built, systematic tests were carried out, and the lift characteristics under different flapping frequencies were analyzed. Through computational modeling and experiments, it has been proven that under the same flapping wing frequency, the T800 carbon fiber composite material frame can significantly improve the stiffness and durability of the flapping wing. In addition, the selection of wing membrane materials has a significant impact on lift performance. Among the test materials, the PET wing film demonstrated excellent stability and lift performance under high-frequency conditions. This research provides crucial experimental evidence for the optimal selection of wing membrane materials for micro flapping-wing aircraft, verifies the application potential of T800 carbon fiber composite materials in micro flapping-wing aircraft, and opens up new avenues for the application of advanced composite materials in high-performance micro flapping-wing aircraft. Full article

Intestinal inflammation involves barrier impairment, immune hyperactivation, and oxidative stress imbalance. Bioactive polysaccharides universally alleviate inflammation via anti-inflammatory, antioxidant, and microbiota-modulating effects, yet exhibit distinct core mechanisms. Elucidating these differences is vital for targeted polysaccharide applications. This research examines distinct regulatory pathways through which diverse bioactive polysaccharides mitigate lipopolysaccharide-triggered intestinal inflammation in male Kunming (KM) mice. This experiment employed Lentinula edodes polysaccharide (LNT), Auricularia auricula polysaccharide (AAP), Cordyceps militaris polysaccharide (CMP), Lycium barbarum polysaccharide (LBP), and Brassica rapa polysaccharide (BRP). The expression levels of biomarkers associated with the TLR4 signaling pathway, oxidative stress, and intestinal barrier function were quantified, along with comprehensive gut microbiota profiling. The results showed that all five polysaccharides alleviated inflammatory responses in mice by inhibiting inflammatory cytokine release, reducing oxidative damage, and modulating gut microbiota, but their modes of action differed: LBP significantly suppressed the TLR-4/MyD88 signaling pathway and its downstream pro-inflammatory cytokine expression, thereby blocking inflammatory signal transduction and reducing oxidative damage; LNT and CMP enhanced the body’s antioxidant capacity by increasing antioxidant enzyme activities and decreasing malondialdehyde (MDA) levels; AAP and BRP enriched Akkermansia (Akk.) within the Verrucomicrobia (Ver.) phylum, upregulating tight junction protein expression to strengthen the intestinal mucosal barrier and indirectly reduce oxidative damage. This research demonstrates that different polysaccharides alleviate inflammation through multi-target synergistic mechanisms: LBP primarily inhibits inflammatory pathways; AAP and BRP focus on intestinal barrier protection and microbiota modulation; and LNT and CMP exert effects via antioxidant enzyme activation. These data support designing polysaccharide blends that leverage complementary inflammatory modulation mechanisms. Full article

Nickel is used in aerospace, military, energy, and chemical sectors. Commercially pure (CP) Ni, and its alloys, including solid-solution strengthened (SSS), precipitation strengthened (PS), and specialty alloys (SA), are widely utilized, typically at elevated temperatures, in corrosive settings and in cryogenic milieu. Ni or Ni-based alloys frequently require welding realized, inter alia, via methods using electric arc and beam power. Tungsten inert gas (TIG) and Electron-beam welding (EBW) have been utilized most often. Friction stir welding (FSW) is the most promising solid-state welding technique for connecting Ni and its alloys. The primary weldability issues related to Ni and its alloys are porosity, as well as hot and warm cracking. CP Ni exhibits superior weldability. It is vulnerable to porosity and cracking during the solidification of the weld metal. Typically, SSS alloys demonstrate superior weldability when compared to PS Ni alloys; however, both types may experience weld metal solidification cracking, liquation cracking in the partially melted and heat-affected zones, as well as ductility-dip cracking (DDC). Furthermore, PS alloys are prone to strain-age cracking (SAC). The weldability of specialty Ni alloys is limited, and brazing might provide a solution. Employing appropriate filler metal, welding settings, and minimal restraint can reduce or avert cracking. Full article

Background/Objectives: This study examines the academic discourse surrounding Critical Incident Stress Debriefing (CISD) and Critical Incident Stress Management (CISM) for first responders using Latent Dirichlet Allocation (LDA) topic modeling. It aims to uncover latent topical structures in the literature and critically evaluate assumptions to identify gaps and limitations. Methods: A corpus of 214 research article abstracts related to CISD/M was gathered from the Web of Science Core Collection. After preprocessing, we used Orange Data Mining software’s LDA tool to analyze the corpus. We tested models ranging from 2 to 10 topics. To guide interpretation and labeling, we evaluated them using log perplexity, topic coherence, and LDAvis visualizations. A four-topic model offered the best balance of detail and interpretability. Results: Four topics emerged: (1) Critical Incident Stress Management in medical and emergency settings, (2) psychological and group-based interventions for PTSD and trauma, (3) peer support and experiences of emergency and military personnel, and (4) mental health interventions for first responders. Key gaps included limited focus on cumulative trauma, insufficient longitudinal research, and variability in procedural adherence affecting outcomes. Conclusions: The findings highlight the need for CISD/M protocols to move beyond event-specific interventions and address cumulative stressors. Recommendations include incorporating holistic, proactive mental health strategies and conducting longitudinal studies to evaluate long-term effectiveness. These insights can help refine CISD/M approaches and enhance their impact on first responders working in high-stress environments. Full article

Infrared small target detection is crucial for military surveillance and autonomous driving. However, complex scenes and weak signal characteristics make the identification of such targets particularly difficult. This study proposes YOLO-SSFA, an enhanced You Only Look Once version 11 (YOLOv11) model with three modules: Scale-Sequence Feature Fusion (SSFF), LiteShiftHead detection head, and Noise Suppression Network (NSN). SSFF improves multi-scale feature representation through adaptive fusion; LiteShiftHead boosts efficiency via sparse convolution and dynamic integration; and NSN enhances localization accuracy by focusing on key regions. Experiments on the HIT-UAV and FLIR datasets show mAP50 scores of 94.9% and 85%, respectively. These findings showcase YOLO-SSFA’s strong potential for real-time deployment in challenging infrared environments. Full article

Infrared small-target detection remains a critical challenge in military reconnaissance, environmental monitoring, forest-fire prevention, and search-and-rescue operations, owing to the targets’ extremely small size, sparse texture, low signal-to-noise ratio, and complex background interference. Traditional convolutional neural networks (CNNs) struggle to detect such weak, low-contrast objects due to their limited receptive fields and insufficient feature extraction capabilities. To overcome these limitations, we propose a Multi-Scale Edge-Aware Convolution (MEAC) module that enhances feature representation for small infrared targets without increasing parameter count or computational cost. Specifically, MEAC fuses (1) original local features, (2) multi-scale context captured via dilated convolutions, and (3) high-contrast edge cues derived from differential Gaussian filters. After fusing these branches, channel and spatial attention mechanisms are applied to adaptively emphasize critical regions, further improving feature discrimination. The MEAC module is fully compatible with standard convolutional layers and can be seamlessly embedded into various network architectures. Extensive experiments on three public infrared small-target datasets (SIRSTD-UAVB, IRSTDv1, and IRSTD-1K) demonstrate that networks augmented with MEAC significantly outperform baseline models using standard convolutions. When compared to eleven mainstream convolution modules (ACmix, AKConv, DRConv, DSConv, LSKConv, MixConv, PConv, ODConv, GConv, and Involution), our method consistently achieves the highest detection accuracy and robustness. Experiments conducted across multiple versions, including YOLOv10, YOLOv11, and YOLOv12, as well as various network levels, demonstrate that the MEAC module achieves stable improvements in performance metrics while slightly increasing computational and parameter complexity. These results validate the MEAC module’s significant advantages in enhancing the detection of small and weak objects and suppressing interference from complex backgrounds. These results validate MEAC’s effectiveness in enhancing weak small-target detection and suppressing complex background noise, highlighting its strong generalization ability and practical application potential. Full article

Compound steering technology has been extensively adopted in military logistics and related applications, owing to its superior maneuverability and enhanced stability compared to conventional systems. To enhance the steering efficiency and dynamic response of distributed-drive unmanned platforms under low driving torque conditions, this study investigates their unique compound steering system. Specifically, a compound steering dynamics model is established, and a hierarchical stability control strategy, along with a model predictive control-based trajectory tracking algorithm, are innovatively proposed. First, a compound steering platform dynamics model is established by combining the Ackermann steering and skid yaw moment methods. Then, a trajectory tracking controller is designed using model predictive control algorithm. Finally, the additional yaw moment is calculated based on the lateral velocity error and yaw rate error, with stability control allocation performed using a fuzzy control algorithm. Comparative hardware-in-the-loop experiments are conducted for compound steering, Ackermann steering, and skid steering. The experimental results show that the compound steering technology enables unmanned platforms to achieve trajectory tracking tasks with a lower torque, faster speed, and higher efficiency. Full article

Background: Drones are rapidly establishing themselves as one of the most critical technologies. Robotics, automated machinery, intelligent manufacturing, and other high-impact technological research and applications bring up pressing ethical, social, legal, and political issues. Methods: The present research aims to present the results of a qualitative investigation that looked at perceptions of the growing socio-ethical conundrums surrounding the development of drone applications. Results: According to the obtained results, participants often share similar opinions about whether different drone applications are approved by the public, regardless of their level of experience. Perceptions of drone applications appear consistent across various levels of expertise. The most notable associations are with military objectives (73%), civil protection (61%), and passenger transit and medical purposes (56%). Applications that have received high approval include science (8.70), agriculture (8.78), and disaster management (8.87), most likely due to their obvious social benefits and reduced likelihood of ethical challenges. Conclusions: The study’s findings can help shape the debate on drone acceptability in particular contexts, inform future research on promoting value-sensitive development in society more broadly, and guide researchers and decision-makers on the use of drones, as people’s attitudes, understanding, and usage will undoubtedly impact future advancements in this technology. Full article

With the growing need for lightweight, durable, and high-performance structures, additively manufactured (AM) polymer composite structures have captured significant attention in the engineering community. These structures offer considerable advantages in various dynamic engineering sectors including automotive, aviation, and military. Thus, this investigation emphasizes the numerical analysis of the dynamic properties and vibration control of AM polylactic acid (PLA) composite structures reinforced with continuous glass fibers (CGFR-PLA) and carbon fibers (CCFR-PLA), with 0°–0° and 0°–90° layer orientations. The findings of this numerical study are compared and validated against earlier published experimental results. Initially, the numerical models were created using the Abaqus CAE 2024, replicating the actual experimental models. The numerical bending modal frequency of each numerical model is determined, and the 0°–0° oriented models exhibited considerably higher values compared to the corresponding 0°–90° models. Significant differences were noted between the numerical and experimental values in the higher modes, mainly due to existence of voids and misalignment in the actual models that were not considered in numerical models. Following this, a numerical amplitude frequency response (AFR) analysis was conducted to observe vibration amplitude variations as a function of frequency. The AFR numerical results demonstrated consistent trends with the experimental results despite differences between the absolute values of both scenarios. Afterwards, vibration amplitude control analysis was performed under the influence of a macro fiber composite (MFC) actuator. The findings from both numerical and experimental cases revealed that vibration control was noticeably higher in 0°–0° oriented structures compared to 0°–90° structures. Experimental models demonstrated higher vibration control effectiveness than the corresponding numerical models. Although significant differences between the numerical and experimental vibration response values were observed in each composite structure, the numerical results exhibited consistent trends with the experiments. This discrepancy is attributed to the challenge of capturing all boundary conditions of the experimental scenario and incorporating them into the numerical simulation. Full article

Wireless sensor networks (WSNs) have found extensive applications in a variety of fields, including military surveillance, wildlife monitoring, industrial process monitoring, and more. The gradual energy depletion of sensor nodes with limited battery energy leads to the dysfunction of some of the nodes, thus creating coverage holes in the monitored area. Coverage holes can cause the network to fail to deliver high-quality data and can also affect network performance and the quality of service. Therefore, the detection and recovery of coverage holes are major issues in WSNs. In response to these issues, we propose a method for detecting and recovering coverage holes in wireless sensor networks. This method first divides the network into equally sized units, and then selects a representative node for each unit based on two conditions, called an agent. Then, the percentage of each unit covered by nodes can be accurately calculated and holes can be detected. Finally, the holes are recovered using the average of the key perceptual intersections as the initial value of the global optimal point of the particle swarm optimization algorithm. Simulation experiments show that the algorithm proposed in this paper reduces network energy consumption by 6.68%, decreases the distance traveled by mobile nodes by 8.51%, and increases the percentage of network hole recovery by 2.16%, compared with other algorithms. Full article

Disruptive color on animals’ bodies can reduce the risk of being caught. This study explores the camouflaging effect of disruptive color when applied to military targets. Disruptive and non-disruptive color patterns were placed on the target surface to form simulation materials. Then, the simulation target was set in woodland-, grassland-, and desert-type background images. The detectability of the target in the background was obtained by collecting eye movement indicators after the observer observed the background targets. The influence of background type (local and global), camouflage pattern type, and target viewing angle on the disruptive-color camouflage pattern was investigated. This study aims to design eye movement observation experiments to statistically analyze the indicators of first discovery time, discovery frequency, and first-scan amplitude in the target area. The experimental results show that the first discovery time of mixed disruptive-color targets in a forest background was significantly higher than that of non-mixed disruptive-color targets (t = 2.54, p = 0.039), and the click frequency was reduced by 15% (p < 0.05), indicating that mixed disruptive color has better camouflage effectiveness in complex backgrounds. In addition, the camouflage effect of mixed disruptive colors on large-scale targets (viewing angle ≥ 30°) is significantly improved (F = 10.113, p = 0.01), providing theoretical support for close-range reconnaissance camouflage design. Full article