Search Results (5,694)

Thrust vectoring technology significantly improves the manoeuvrability and environmental adaptability of unmanned aerial vehicles by dynamically regulating the direction and magnitude of thrust. In this paper, the principles and applications of mechanical thrust vectoring technology, fluidic thrust vectoring technology and the distributed electric propulsion system are systematically reviewed. It is shown that the mechanical vector nozzle can achieve high-precision control but has structural burdens, the fluidic thrust vectoring technology improves the response speed through the design of no moving parts but is accompanied by the loss of thrust, and the distributed electric propulsion system improves the hovering efficiency compared with the traditional helicopter. Addressing multi-physics coupling and non-linear control challenges in unmanned aerial vehicles, this paper elucidates the disturbance compensation advantages of self-disturbance rejection control technology and the optimal path generation capabilities of an enhanced path planning algorithm. These two approaches offer complementary technical benefits: the former ensures stable flight attitude, while the latter optimises flight trajectory efficiency. Through case studies such as the Skate demonstrator, the practical value of these technologies in enhancing UAV manoeuvrability and adaptability is further demonstrated. However, thermal management in extreme environments, energy efficiency and lack of standards are still bottlenecks in engineering. In the future, breakthroughs in high-temperature-resistant materials and intelligent control architectures are needed to promote the development of UAVs towards ultra-autonomous operation. This paper provides a systematic reference for the theory and application of thrust vectoring technology. Full article

The heavy mineral-rich wadi deposits sourced from various wadis close to Gabal Homret Waggat in the central eastern Desert of Egypt are being analyzed to assess their genesis and paleoenvironment. This study integrates remote sensing (ALOS/PALSAR DEM and ASTER imagery), mineralogical, and geochemical analyses (XRF and SEM-EDX). Remote sensing analysis (ASTER and ALOS/PALSAR) delineated three main watersheds and identified granitic plutons as the primary source rocks. Mineralogical analysis revealed a diverse heavy mineral assemblage, including zircon, rutile, ilmenite, magnetite, staurolite, and sillimanite, indicative of a provenance dominated by granitic and metamorphic rocks. Grain size analysis shows that the samples range from very platykurtic to extremely leptokurtic (Kg: 0.598–5.350 φ), indicating deposition in predominantly fluvial environments. Geochemical data show enrichment in SiO₂, Al₂O₃, K₂O, and Na₂O, indicating a felsic (granitic) source with low Chemical Index of Alteration (CIA: 41.89–51.83) and Plagioclase Index of Alteration (PIA: 37.97–52.78) values, and indicating that the source rocks show low to moderate chemical weathering. Tectonic discrimination diagrams suggest that the source rocks were formed in a continental island arc or active continental margin, consistent with the Arabian–Nubian Shield. The presence of economically valuable minerals like zircon and rare-earth-element-bearing monazite and columbite highlights the significant resource potential of these placer deposits. Full article

The origin of life initiated an evolutionary continuum yielding biologically optimized systems capable of operating under extreme environmental constraints. Biomimetics, defined as the systematic abstraction and transfer of biological principles into engineering domains, has become a strategic design paradigm for addressing the multifactorial challenges of space systems. This study introduces two core contributions to formally establish the discipline of Aerospace Bionic Robotics (ABR): First, it elucidates the relevance of biologically derived functionalities such as autonomy, adaptability, and multifunctionality to enhance the efficiency of space robotic platforms operating in microgravity environments. Second, it proposed the BEAM-D (Biomimetic Engineering and Aerospace Mechatronics Design), a standard for the development of Aerospace Bionic Robotics. By integrating biological abstraction levels (morphological, functional, and behavioral) with engineering protocols including ISO, VDI, and NASA’s TRL, BEAM-D enables a structured design pathway encompassing subsystem specification, cyber–physical integration, in situ testing, and full-scale mission deployment. It is implemented through a modular BEAM-DX framework and reinforced by iterative BIOX design steps. This study thus establishes formalized bio-inspired design tools for advanced orbital and planetary robotic systems capable of sustained autonomous operations in deep space exploration scenarios. Full article

The Mongolian wild ass (Equus hemionus hemionus) is a flagship species of the desert-steppe ecosystem in Asia, and understanding its strategies for coping with cold environments is vital for both revealing its survival mechanisms and informing conservation efforts. In this study, we employed metagenomic sequencing to characterize the composition and functional potential of the gut microbiota, and applied DNA metabarcoding of the chloroplast trnL (UAA) g–h fragment to analyze dietary composition, aiming to reveal seasonal variations and the interplay between dietary plant composition and gut microbial communities. In the cold season, Bacteroidota and Euryarchaeota were significantly enriched, suggesting enhanced fiber degradation and energy extraction from low-quality forage. Moreover, genera such as Bacteroides and Alistipes were also significantly enriched and associated with short-chain fatty acid (SCFA) metabolism, bile acid tolerance, and immune modulation. In the cold season, higher Simpson index values and tighter principal coordinates analysis (PCoA) clustering indicated a more diverse and stable microbiota under harsh environmental conditions, which may represent an important microecological strategy for the host to cope with extreme environments. Functional predictions based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) further indicated upregulation of metabolic and signaling pathways, including ABC transporters, two-component systems, and quorum sensing, suggesting multi-level microbial responses to low temperatures and nutritional stress. trnL-based plant composition analysis indicated seasonal shifts, with Tamaricaceae detected more in the warm season and Poaceae, Chenopodiaceae, and Amaryllidaceae detected more in the cold season. Correlation analyses revealed that dominant microbial phyla were associated with the degradation of fiber, polysaccharides, and plant secondary metabolites, which may help maintain host energy and metabolic homeostasis. Despite the limited sample size and cross-sectional design, our findings highlight that gut microbial composition and structure may be important for host adaptation to cold environments and may also serve as a useful reference for future studies on the adaptive mechanisms and conservation strategies of endangered herbivores, including the Mongolian wild ass. Full article

Conductive hydrogels (CHs) have attracted significant attention in the fields of flexible electronics, human–machine interaction, and electronic skin (e-skin) due to their self-adhesiveness, environmental stability, and multi-stimuli responsiveness. However, integrating these diverse functionalities into a single conductive hydrogel system remains a challenge. In this study, polyvinyl alcohol (PVA) and polyacrylamide (PAM) were used as the dual-network matrix, lithium chloride and MXene were added, and a simple immersion strategy was adopted to synthesize a multifunctional MXene-based conductive hydrogel in a glycerol/water (1:1) binary solvent system. A subsequent investigation was then conducted on the hydrogel. The prepared PVA/PAM/LiCl/MXene hydrogel exhibits excellent tensile properties (~1700%), high electrical conductivity (1.6 S/m), and good self-healing ability. Furthermore, it possesses multimodal sensing performance, including humidity sensitivity (sensitivity of −1.09/% RH), temperature responsiveness (heating sensitivity of 2.2 and cooling sensitivity of 1.5), and fast pressure response/recovery times (220 ms/230 ms). In addition, the hydrogel has successfully achieved real-time monitoring of human joint movements (elbow and knee bending) and physiological signals (pulse, breathing), as well as enabled monitoring of spatial pressure distribution via a 3 × 3 sensor array. The performance and versatility of this hydrogel make it a promising candidate for next-generation flexible sensors, which can be applied in the fields of human health monitoring, electronic skin, and human–machine interaction. Full article

Underground mining faces unique challenges in equipment maintenance due to extreme operating conditions and intensive use, which limit the effectiveness of traditional methods. This study proposes a predictive maintenance (PdM) framework based on artificial intelligence (AI) to optimize efficiency and reduce costs, focusing on early fault detection. The methodology integrates IoT sensors to monitor key parameters (temperature, pressure, oil analysis, and wear) in real time, combined with machine learning models to identify predictive patterns. The results demonstrate an 8% reduction in maintenance costs and a 10% increase in equipment availability, validating the system’s ability to anticipate failures and minimize unplanned downtime. It is concluded that this approach not only enhances productivity but also raises safety standards, offering a scalable model for critical industrial environments. The findings are supported by empirical data collected from actual operations, with no theoretical extrapolations. Full article

Urban school environments often face significant thermal discomfort due to extensive paved surfaces, limited vegetation, and outdated building designs. This study examines how green spaces can mitigate temperature extremes and improve thermal comfort at two secondary schools in Coimbra, Portugal: Escola Secundária José Falcão (ESJF) and Escola Secundária D. Dinis (ESDD). Using a mixed-methods approach that combined school community surveys with on-site microclimatic measurements, we integrated user feedback on comfort with data on temperature and humidity variations across different indoor and outdoor spaces. Results revealed that tree-shaded areas consistently maintained lower air temperatures and higher relative humidity than unshaded zones, which experienced intense heat accumulation—up to a 5 °C difference. At ESJF, the older infrastructure and large asphalt surfaces led to severe heat retention, with east-facing classrooms recording the highest indoor temperatures. ESDD’s pavilion-style layout and existing green spaces provided comparatively better thermal conditions, although insufficient vegetation maintenance and limited shade reduced their effectiveness. The findings demonstrate a clear correspondence between the school community’s perceptions of thermal comfort and the measured microclimatic data. Vegetation—particularly deciduous trees—plays a critical role in cooling the school microclimate through shading and evapotranspiration. Strategic interventions such as expanding tree cover in high-exposure areas, installing green roofs and walls, and carefully selecting species can significantly reduce temperature extremes and improve outdoor usability. In addition, fostering environmental education and participatory co-design programs can encourage sustainable behaviors within the school community, underlining the importance of inclusive, nature-based solutions for climate adaptation. This research highlights that integrating green infrastructure in school design and management is a cost-effective strategy for thermal regulation. Green spaces, when co-designed with community involvement, not only enhance climate resilience and student well-being but also contribute to broader sustainable urban development goals. Full article

The harsh conditions of the space environment necessitate advanced materials capable of withstanding extreme temperature fluctuations and ultraviolet (UV) radiation. While epoxy-based composites are widely utilized in aerospace due to their favorable strength-to-weight ratio, they are prone to degradation, especially under prolonged high-energy UV-C exposure. This study investigated the mechanical and chemical stability of epoxy composites reinforced with nanosilica at 0, 2, 5, and 10 wt% before and after UV-C irradiation. Dynamic mechanical analysis (DMA) revealed that increased nanosilica content enhanced the storage modulus below the glass transition temperature (Tg) but reduced both Tg and the damping factor. Following UV-C exposure, all samples showed a decrease in storage modulus and Tg; however, composites with higher nanosilica content maintained better property retention. Frequency sweeps corroborated these findings, indicating improved instantaneous modulus but accelerated relaxation with increased nanosilica. Fourier-transform infrared (FTIR) spectroscopy of UV-C-exposed samples demonstrated significant oxidation and carboxylic group formation in neat epoxy, contrasting with minimal spectral changes in nanosilica-modified composites, signifying improved chemical resistance. Overall, nanosilica incorporation substantially enhances the thermomechanical and oxidative stability of epoxy composites under simulated space conditions, highlighting their potential for more durable performance in low Earth orbit applications. Full article

Atmospheric turbulence severely limits the stability and reliability of free-space optical (FSO) uplinks by inducing wavefront distortions and random intensity fluctuations. This study investigates the use of reinforcement learning (RL) with beacon-based feedback for adaptive beam shaping in a spatial light modulator (SLM)-controlled FSO link. The RL agent dynamically adjusts phase patterns to maximize received signal strength, while the beacon channel provides turbulence estimates that guide the optimization process. Experiments under low, moderate, and high turbulence levels demonstrate that incorporating beacon feedback can enhance link stability in severe conditions, reducing signal variability and suppressing extreme fluctuations. In low-turbulence scenarios, the performance is comparable to non-feedback operation, whereas under high turbulence, beacon-assisted control consistently achieves lower coefficients of variation and improved bit error rate (BER) performance. Under high turbulence replay experiments—where the best-performing RL-learned phase patterns are reapplied without learning—further show that configurations trained with feedback retain robustness, even without real-time turbulence measurements under high turbulence. These results highlight the potential of integrating contextual feedback with RL to achieve turbulence-resilient and stable optical uplinks in dynamic atmospheric environments. Full article

As lunar exploration missions advance, the need for safe and sustainable in situ energy systems has become increasingly critical. This study investigates the thermoelectric performance of Bi₂Te₃-based thermoelectric materials under the natural temperature variations on the lunar surface, aiming to illustrate the potential of thermoelectric generation technology in power supply for a crewed moon base. A numerical approach was employed to assess the energy conversion behavior and optimize the geometric design of a thermoelectric module couple consisting of a P-leg and N-leg. The results indicate that Bi₂Te₃-based modules exhibit promising functionality under cryogenic conditions, highlighting their potential as an in situ power source during the long lunar night. Furthermore, geometric optimization was shown to significantly enhance the overall thermoelectric performance. The present study illustrates that TEG technology offers a viable pathway toward reliable energy generation in extreme lunar environments, supporting future mission sustainability. Full article

To address low training efficiency and poor reconstruction in traditional K Singular Value Decomposition (KSVD) for compressive sensing of shock wave signals, this study proposes an improved algorithm, DH-KSVD, integrating dynamic pruning and hybrid coding. The dynamic pruning mechanism eliminates redundant atoms according to their contributions and adaptive thresholds, while incorporating residual features to enhance dictionary compactness and training efficiency. The hybrid sparse constraint integrates the sparsity of ${\ell }_0$-Orthogonal Matching Pursuit (OMP) with the noise robustness of ${\ell }_1$-Least Absolute Shrinkage and Selection Operator (LASSO), dynamically adjusting their relative weights to enhance both coding quality and reconstruction stability. Experiments on typical shock wave datasets show that, compared with Discrete Cosine Transform (DCT), KSVD, and feature-based segmented dictionary methods (termed CC-KSVD), DH-KSVD reduces average training time by 46.4%, 31%, and 13.7%, respectively. At a Compression Ratio (CR) of 0.7, the Root Mean Square Error (RMSE) decreases by 67.1%, 65.7%, and 36.2%, while the Peak Signal-to-Noise Ratio (PSNR) increases by 35.5%, 39.8%, and 11.8%, respectively. The proposed algorithm markedly improves training efficiency and achieves lower RMSE and higher PSNR under high compression ratios, providing an effective solution for compressing long-duration, transient shock wave signals. Full article

Background: Neuromuscular training is widely implemented in professional football to enhance performance and reduce injury risk. Although unstable surfaces are commonly used for proprioceptive and rehabilitation purposes, limited evidence supports their effectiveness in improving sport-specific force production and stability in elite athletes. This study aimed to compare the effects of multicomponent neuromuscular training performed on stable versus unstable surfaces on unilateral force production, mobility, and agility in elite male soccer players. Methods: Twenty-seven professional male soccer players from the Spanish first division were randomly assigned to either a stable surface group (SSG; n = 14) or an unstable surface group (USG; n = 13). Both groups completed a 10-week intervention in addition to their regular training routines. Pre- and post-intervention assessments included dorsiflexion range of motion (DFt), Y-Balance Test (YBT), single-leg countermovement jump (SLCMJ), single-leg hop for distance (SLH), side-hop (SH), Speedy Jump (SpJ), Agility T-test (TT), and the Lower Extremity Functional Test (LEFT). A two-way repeated-measures ANOVA and Hedges’ g effect sizes were used for statistical analysis. Results: The SSG showed significant improvements in most performance variables, including DFt, YBT, SLH, SH, SpJ, TT, and LEFT (percent change range: 1.6% to 9.8%; Hedges’ g ranging from 0.52 to 2.57). The USG showed limited improvements, with significant changes only in LEFT (percent change = 1.18%; Hedges’ g = 0.53). Notably, the stable surface group demonstrated enhanced force production and agility, particularly in the non-dominant limb. Conclusions: Multicomponent neuromuscular training on stable surfaces appears more effective than training on unstable surfaces for improving unilateral strength, mobility, and agility in elite soccer players. These findings suggest that stable surface training may provide superior performance benefits and should be considered a priority in high-performance environments. Full article

Polyimide composites represent a class of advanced materials with remarkable mechanical robustness and thermal stability, making them highly suitable for applications in extreme environments. Their unique ability to maintain performance under high temperatures and corrosive conditions, combined with a favorable strength-to-weight ratio, positions them as critical components in aerospace, electronics, and automotive systems. Several leading aerospace and electronics corporations have made significant investments in incorporating polyimide composites into their products, indicating the material’s transformative potential. This review paper provides an overview of mechanical and thermal behaviors of polyimide composites, summarizing recent developments and research trends. It examines the influence of various reinforcements, processing techniques, and composite architectures on material performance under mechanical loading and thermal stress. The paper synthesizes findings from experimental studies and modeling efforts to highlight the critical factors affecting strength, durability, and thermal stability. Discussion and recommendations regarding applications in aerospace, electronics, and other high-temperature environments are provided, emphasizing the challenges and opportunities presented by these advanced materials. This review adopts a broad scope to reflect the interdisciplinary nature of polyimide research. Due to gaps in literature, this work aims to provide a foundational overview that supports future, more specialized investigations. Full article

Marine heatwaves (MHWs) pose a serious threat to the marine ecosystems and fishery resources in the East China Sea (ECS). Based on National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature High Resolution version 2 data, this study investigated the regional divergence in long-term trends of MHWs in the ECS from 1982 to 2023. The principal findings were as follows. Concerning MHWs, the coastal waters of China from northern Jiangsu coast to northeast of Taiwan Island experienced a relatively high annual average frequency, the longest duration, largest number of total days, strongest intensity, and the most pronounced seasonal signals. Additionally, the areas along the Kuroshio path showed significant levels of frequency, duration, and total days, but with comparatively weak intensity. In the empirical orthogonal function (EOF) analysis, EOF1 of the total days and cumulative intensity exhibited notable variation along the path of the Kuroshio and its offshoots, and in Chinese coastal areas. EOF2 showed significantly more conspicuous variation in areas extending from the Yangtze River Estuary to the northern Jiangsu coast. Furthermore, the MHW indices generally showed a positive trend in the ECS from 1982 to 2023. Importantly, the regions with high annual average MHW indices were also characterized by a significantly positive increasing trend. Moderate (79.10%) and strong (19.94%) events were most prevalent, whereas severe (0.82%) and extreme (0.14%) events occurred infrequently. The enhanced solar radiation and the reduced latent heat loss were the main contributing factors of MHWs in the ECS. These findings provide valuable insights into the ecological environment and resources of the ECS as a marine pastoral area. Full article

The application of time series models for forecasting environmental variables such as precipitation is essential for understanding climatic patterns and supporting sustainable urban planning in environments characterized by high or moderate levels of risk. This study aims to evaluate and optimize time series forecasting models for rainfall prediction in Barranquilla, Colombia. To this end, five models were applied, namely, Simple Moving Average (SMA), Weighted Moving Average (WMA), Exponential Smoothing (ES), and multiplicative and additive Holt–Winters models, using 139 monthly precipitation records from the IDEAM database covering the period 2013–2025. Model accuracy was evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE), and nonlinear optimization techniques were applied to estimate smoothing and weighting parameters for improved accuracy. The results showed that optimization significantly enhances model performance, particularly in the multiplicative Holt–Winters model, which achieved the lowest errors, with a minimum MAE of 75.33 mm and an MSE of 9647.07. The comparative analysis with previous studies demonstrated that even simple models can yield substantial improvements when properly optimized. Furthermore, forecasts optimized using MAE were more stable and consistent, whereas those optimized with MSE were more sensitive to extreme variations. Overall, the findings confirm that seasonal models with optimized parameters offer superior predictive capacity, making them valuable tools for hydrological risk management. Full article