Search Results (155)

Given the fast-evolving context of electrical vertical takeoff and landing vehicles (eVTOL) based on the concept of tiltrotor aircraft, this work describes a framework aimed at the preliminary aerodynamic design and optimization of innovative lifting surfaces of such rotorcraft vehicles. In particular, a multiobjective optimization process was applied to the design of a wing extension representing an innovative feature recently investigated to improve the aerodynamic performance of a tiltrotor aircraft wing. The wing/proprotor configurations, selected using a Design Of Experiment (DOE) approach, were simulated by the mid-fidelity aerodynamic code DUST, which used a vortex-particle method (VPM) approach to model the wing/rotor wakes. A linear regression model accounting for nonlinear interactions was used by an evolutionary algorithm within a multiobjective optimization framework, which provided a set of Pareto-optimal solutions for the wing extension, maximizing both wing and rotor efficiency. Moreover, the present work highlighted how the use of a fast and reliable numerical modeling for aerodynamics, such as the VPM approach, enhanced the capabilities of an optimization framework aimed at achieving a more accurate preliminary design of innovative features for rotorcraft configurations while taking into account the effects of the aerodynamic interaction between wings and proprotors. Full article

This paper presents the design of a nonlinear Multi-Input Multi-Output (MIMO) Feedback Linearization Controller (FLC) for the longitudinal dynamics of a fixed-wing UAV. The proposed approach employs dynamic extension to achieve Feedback Linearization in a fourth-order longitudinal model, offering a more compact alternative to existing high-order formulations. The controller ensures the accurate tracking of predefined airspeed and flight path angle references, that is, the control of the magnitude and direction of the velocity vector using engine thrust and pitch moment as control inputs. Additionally, this study determines the region of attraction in which the controller design remains well-defined. This analysis provides critical insights for selecting feasible airspeed and flight path angle references, helping to prevent conditions that can lead to instability or undesirable behaviors, such as the need for negative thrust. Numerical simulations validate the effectiveness of the proposed method in handling the aircraft’s nonlinear dynamics and maintaining stable flight performance. Full article

Drones have rapidly emerged as transformative tools in landscape research, enabling high-resolution spatial data acquisition, real-time environmental monitoring, and advanced modelling that surpass the limitations of traditional methodologies. This scoping review systematically explores and synthesises the technological applications of drones within the context of landscape studies, addressing a significant gap in the integration of Uncrewed Aerial Systems (UASs) into environmental and spatial planning disciplines. The study investigates the typologies of drone platforms—including fixed-wing, rotary-wing, and hybrid systems—alongside a detailed examination of sensor technologies such as RGB, LiDAR, multispectral, and hyperspectral imaging. Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, a comprehensive literature search was conducted across Scopus, Web of Science, and Google Scholar, utilising predefined inclusion and exclusion criteria. The findings reveal that drone technologies are predominantly applied in mapping and modelling, vegetation and biodiversity analysis, water resource management, urban planning, cultural heritage documentation, and sustainable tourism development. Notably, vegetation analysis and water management have shown a remarkable surge in application over the past five years, highlighting global shifts towards sustainability-focused landscape interventions. These applications are critically evaluated in terms of spatial efficiency, operational flexibility, and interdisciplinary relevance. This review concludes that integrating drones with Geographic Information Systems (GISs), artificial intelligence (AI), and remote sensing frameworks substantially enhances analytical capacity, supports climate-resilient landscape planning, and offers novel pathways for multi-scalar environmental research and practice. Full article

Obstacle avoidance during high-speed, low-altitude flight remains a significant challenge for unmanned aerial vehicles (UAVs), particularly in unfamiliar environments where prior maps and heavy onboard sensors are unavailable. To address this, we present an entropy-aware deep reinforcement learning framework that enables fixed-wing UAVs to navigate safely using only monocular onboard cameras. Our system features a lightweight, single-frame depth estimation module optimized for real-time execution on edge computing platforms, followed by a reinforcement learning controller equipped with a novel reward function that balances goal-reaching performance with path smoothness under fixed-wing dynamic constraints. To enhance policy optimization, we incorporate high-quality experiences from the replay buffer into the gradient computation, introducing a soft imitation mechanism that encourages the agent to align its behavior with previously successful actions. To further balance exploration and exploitation, we integrate an adaptive entropy regularization mechanism into the Proximal Policy Optimization (PPO) algorithm. This module dynamically adjusts policy entropy during training, leading to improved stability, faster convergence, and better generalization to unseen scenarios. Extensive software-in-the-loop (SITL) and hardware-in-the-loop (HITL) experiments demonstrate that our approach outperforms baseline methods in obstacle avoidance success rate and path quality, while remaining lightweight and deployable on resource-constrained aerial platforms. Full article

Swarm Intelligence (SI) algorithms were applied widely in solving complex optimization problems because they are simple, flexible, and efficient. The current paper proposes a new SI algorithm, which is based on the bird-like actions of swallows, which have highly synchronized behaviors of foraging and migration. The optimization algorithm (SWSO) makes use of these behaviors to boost the ability of exploration and exploitation in the optimization process. Unlike other birds, swallows are known to be so precise when performing fast directional alterations and making intricate aerial acrobatics during foraging. Moreover, the flight patterns of swallows are very efficient; they have extensive capabilities to transition between flapping and gliding with ease to save energy over long distances during migration. This allows instantaneous changes of wing shape variations to optimize performance in any number of flying conditions. The model used by the SWSO algorithm combines these biologically inspired flight dynamics into a new computational model that is aimed at enhancing search performance in rugged terrain. The design of the algorithm simulates the swallow’s social behavior and energy-saving behavior, converting it into exploration, exploitation, control mechanisms, and convergence control. In order to verify its effectiveness, (SWSO) is applied to many benchmark problems, such as unimodal, multimodal, fixed-dimension functions, and a benchmark CEC2019, which consists of some of the most widely used benchmark functions. Comparative tests are conducted against more than 30 metaheuristic algorithms that are regarded as state-of-the-art, developed so far, including PSO, MFO, WOA, GWO, and GA, among others. The measures of performance included best fitness, rate of convergence, robustness, and statistical significance. Moreover, the use of (SWSO) in solving real-life engineering design problems is used to prove (SWSO)’s practicality and generality. The results confirm that the proposed algorithm offers a competitive and reliable solution methodology, making it a valuable addition to the field of swarm-based optimization. Full article

Background: The varied shape and size of the sphenoid sinuses result in a highly variable degree of extension, described in different ways in the literature. The aim of the study was to create the authors’ classification of the sphenoid sinus extensions into the sphenoid bone processes. Methods: The study was retrospective, based on the results of head CT scans. The study group comprised 432 children, aged from birth to 18 years. Three types of sphenoid sinus extension into the sphenoid bone processes were proposed. Pneumatization of the lesser wing (type I), of the greater wing (type II), and of the pterygoid process (type III). Three subtypes were defined for each type. Pneumatization of the lesser wing in relation to the optic canal: only above (Ia), only below (Ib), and simultaneously above and below (Ic). Pneumatization of the greater wing in relation to the foramen rotundum: only above (IIa), only below (IIb), and simultaneously above and below (IIc). Pneumatization of the pterygoid process in relation to the pterygoid canal: only laterally (IIIa), only medially (IIIb), and simultaneously laterally and medially (IIIc). Results: Pneumatization of the lesser wings was observed in 19.44%, of the greater wings in 36.11%, and of the pterygoid processes in 25.00 of % children. As a result of the analysis conducted in accordance with the proposed classification, bilateral pneumatization of the lesser wings (type I), greater wings (type II), and pterygoid processes (type III) were found to occur more frequently than unilateral pneumatization. In the case of rare subtypes (Ia, Ic, IIa, Iic, or IIIb), a given subtype was observed to occur more frequently unilaterally. Conclusions: In the conducted analysis, we presented the authors’ classification of sphenoid sinus extensions into the sphenoid bone processes. Full article

Wind estimation plays a crucial role in atmospheric boundary layer research and aviation flight safety. Fixed-wing UAVs enable rapid and flexible detection across extensive boundary layer regions. Traditional meteorological fixed-wing UAVs require either additional wind measurement sensors or sustained turning maneuvers for wind estimation, increasing operational costs while inevitably reducing mission duration and coverage per flight. This paper proposes a real-time wind estimation method based on an Unscented Kalman Filter (UKF) without aerodynamic sensors. The approach utilizes only standard UAV avionics—GNSS, pitot tube, and Inertial Measurement Unit (IMU)—to estimate wind fields. To validate accuracy, the method was integrated into a meteorological UAV equipped with a wind vane sensor, followed by multiple flight tests. Comparison with wind vane measurements shows real-time wind speed errors below 1 m/s and wind direction errors within 20° (0.349 rad). Results demonstrate the algorithm’s effectiveness for real-time atmospheric boundary layer wind estimation using conventional fixed-wing UAVs. Full article

In recent years, effective trajectory planning has been developed to promote the extensive application of unmanned aerial vehicles (UAVs) in various domains. However, the actual operation of UAVs in complex environments presents significant challenges to their trajectory planning, particularly in maintaining task reliability and ensuring safety. To overcome these challenges, this review presents a comprehensive summary of various trajectory planning techniques currently applied to UAVs based on the emergence of intelligent algorithms, which enhance the adaptability and learning ability of UAVs and offer innovative solutions for their application in complex environments. Firstly, the characteristics of different UAV types, including fixed-wing, multi-rotor UAV, single-rotor UAV, and tilt-rotor UAV, are introduced. Secondly, the key constraints of trajectory planning in complex environments are summarized. Thirdly, the research trend from 2010 to 2024, together with the implementation, advantages, and existing problems of machine learning, evolutionary algorithms, and swarm intelligence, are compared. Based on these algorithms, the related applications of UAVs in complex environments, including transportation, inspection, and other tasks, are summarized. Ultimately, this review provides practical guidance for developing intelligent trajectory planning methods for UAVs to achieve the minimal amount of time spent on computation, efficient dynamic collision avoidance, and superior task completion ability. Full article

In China, Anostostomatidae is represented by a single tribe, Anabropsini; two genera; and 33 species. Although extensive research has been conducted on Anabropsini, the monophyly of this tribe within Anostostomatidae remains unverified. Furthermore, the phylogenetic relationships within Anabropsis remain under debate. To address these gaps, we sequenced and annotated the mitochondrial genomes of five Anabropsini species to investigate their mitochondrial characteristics and phylogenetic positions and clarify the relationships among Anabropsis subgenera. The total mitochondrial length of the five species ranged from 15,985 bp to 16,423 bp and contained 13 protein-coding genes, 22 tRNAs, 2 rRNAs, and 1 control region. A grouped analysis of selection pressure on Anabropsis revealed that the Ka/Ks values for alate and apterous forms are not significantly different, suggesting that using wing length alone as the basis for dividing subgenera within Anabropsis may be unreliable. Tertiary structure modeling of proteins showed that the variable sites were concentrated in α-helix regions. Phylogenetic trees were reconstructed using the Bayesian inference and maximum likelihood methods and were based on two better datasets, namely, PCG123 (all codon positions of the PCGs) and PCG123 + 2R (all codon positions of PCGs, 12SrRNA, and 16SrRNA). The results indicate that the Chinese Anabropsini is paraphyletic, whereas Anabropsis is monophyletic, with a stable subgeneric topology. Full article

This paper introduces a novel recursive algorithm for inverting matrix polynomials, developed as a generalized extension of the classical Leverrier–Faddeev scheme. The approach is motivated by the need for scalable and efficient inversion techniques in applications such as system analysis, control, and FEM-based structural modeling, where matrix polynomials naturally arise. The proposed algorithm is fully numerical, recursive, and division free, making it suitable for large-scale computation. Validation is performed through a finite element simulation of the transverse vibration of a fighter aircraft wing. Results confirm the method’s accuracy, robustness, and computational efficiency in computing characteristic polynomials and adjugate-related forms, supporting its potential for broader application in control, structural analysis, and future extensions to structured or nonlinear matrix systems. Full article

Aircraft and fixed-wing drones, designed to perform vertical take-off and landing (VTOL), often incorporate unconventional configurations that offer unique capabilities but simultaneously pose significant challenges in flight mechanics modeling, whose reliability strongly depends on the correct tuning of the inertial and aerodynamic parameters. Having a good characterization of the aerodynamics represents a critical issue, especially in the design and optimization of unconventional aircraft configurations, when, indeed, one is bound to employ empirical or semi-empirical methods, devised for conventional geometries, that struggle to capture complex aerodynamic interactions. Alternatives such as high-fidelity computational fluid dynamics (CFD) simulations, although more accurate, are typically expensive and impractical for both preliminary design and lofting optimization. This work introduces a procedure that exploits multiple analyses conducted through semi-empirical methodologies implemented in the USAF Digital DATCOM to develop a flight mechanics model for fixed-wing unmanned aerial vehicles (UAVs). The reference UAV chosen to test the proposed procedure is the Dragonfly DS-1, an electric VTOL UAV developed by Overspace Aviation, featuring a three-surface configuration. The accuracy of the polar data, i.e., the lift and drag coefficients, is assessed through comparisons with computational fluid dynamics simulations and flight data. The main discrepancies are found in the drag estimation. The present work represents a preliminary investigation into the possible extension of semi-empirical methods, consolidated for traditional configurations, to unconventional aircraft so as to support early-stage UAV design. Full article

Gynopara is a specific winged type in the life history of Aphis gossypii. As a key reproduction mode between parthenogenesis and sexual reproduction, it lays sexual females in late autumn. However, little is known about gynoparae because of its rare presence in the wild and the lack of its detailed descriptions. In this study, we investigated the morphological characteristics, ovary maturation process, and key signaling pathways during the development of gynoparae of A. gossypii. With the extension of development stage, the gynoparae exhibited gradually enlarged and thickened abdomen, deepened color. Obviously differentiated wing primordia in the second instar nymphs. Two nested U-shaped zones containing a series of waxy secreta spots were present on the abdomen of the fourth instar nymphs, and these morphological characteristics could be used as important markers for identifying gynoparae. Temporal transcriptomic analysis suggested that juvenile hormone (JH) might be involved in regulating the development of gynoparae. After the application JH analog kinoprene to the first instar nymphs, almost all treated individuals failed to complete wing differentiation, and most lost the ability to produce progeny, suggesting the destructive effect of JH on wing differentiation and reproduction of gynoparae. Moreover, exogenous addition of kinoprene also significantly altered the expression levels of four key genes responsible for the synthesis and degradation of JH. Our findings reveal the mechanism by which JH regulated wing differentiation and reproductive capacity of gynoparae, which lay a foundation for the further research on reproduction mode switch in aphids in the future. Full article

A newly designed tilt-wing unmanned aerial vehicle (Tilt-wing UAV) requires a unified control strategy across rotary-wing, fixed-wing, and transition modes, introducing significant challenges. Existing control strategies typically rely on accurate modeling or extensive parameter tuning, which limits their adaptability to dynamically changing flight configurations. Although online reinforcement learning algorithms offer adaptability, they depend on real-world exploration, posing considerable safety and cost risks for safety-critical UAV applications. To address this challenge, we propose Temporal Sequence Constrained Q-learning (TSCQ), an offline RL framework that integrates an encoder–decoder with recurrent networks to capture temporal dependencies. The policy is further constrained within an offline dataset collected via hardware-in-the-loop simulation using a variational autoencoder, and a sequence-level prediction mechanism is introduced to ensure temporal consistency across action trajectories, thereby mitigating extrapolation error while preserving data fidelity. Experimental results demonstrate that TSCQ significantly outperforms gain scheduling, Model Predictive Control (MPC), and Batch-Constrained Q-learning (BCQ), reducing the RMSE of pitch angle by up to 53.3% and vertical velocity RMSE by approximately 33%. These findings underscore the potential of data-driven, safety-aware offline RL paradigms to enable robust and generalizable control strategies for tilt-wing UAVs. Full article

Stiffened panels are extensively used in aerospace applications, particularly in wing and fuselage sections, due to their favorable strength-to-weight ratio under in-plane loading conditions. This research employs the commercial finite element software Ansys-19 to analysis the critical buckling and ultimate collapse load of an aluminum stiffened panel having a dimension of 1244 mm (Length) × 957 mm (width) × 3.5 mm (thickness), with three stiffener blades located 280 mm away from each other. Both the critical buckling load and post-buckling ultimate failure load of the panel are validated against the experimental data found in the available literature, where the edges towards the length are clamped and simply supported, and the other two edges are free. For nonlinear buckling analysis, a plasticity power law is adopted with a small geometric imperfection of 0.4% at the middle of the panel. After the numerical validation, the investigation is further carried out considering four different lateral pressures, specifically 0.013 MPa, 0.065 MPa, 0.085 MPa, and 0.13 MPa, along with the compressive loading boundary conditions. It was found that even though the pressure application of 0.013 MPa did not significantly impact the critical buckling load of the panel, the ultimate collapse load was reduced by 18.5%. In general, the ultimate collapse load of the panel was severely affected by the presence of lateral pressure while edge compressing. Three opening shapes—namely, square, circular, and rectangular/hemispherical—were also investigated to understand the behavior of the panel with openings. It was found that the openings significantly affected the critical buckling load and ultimate collapse load of the stiffened panel, with the lateral pressure also contributing to this effect. Finally, in critical areas with higher lateral pressure load, a titanium panel can be a good alternative to the aluminum panel since it can provide almost twice to thrice better buckling stability and ultimate collapse load to the panels with a weight nearly 1.6 times higher than aluminum. These findings highlight the significance of precision manufacturing, particularly in improving and optimizing the structural efficiency of stiffened panels in aerospace industries. Full article

In recent years, face recognition under occluded or corrupted conditions has emerged as a prominent research topic. The advancement in sparse sampling techniques based on regression analysis has provided a novel solution to this challenge. Currently, numerous regression-based sparse sampling models have been investigated by researchers to address this problem. However, the recognition accuracy of most existing models deteriorates significantly when handling heavily occluded or severely corrupted facial images. To overcome this limitation, this paper proposes a wing-constrained sparse coding (WCSC) model and its weighted variant (weighted wing-constrained sparse coding, WWCSC) for robust face recognition in complex scenarios. The corresponding minimization problems are solved using the alternating direction method of multipliers (ADMM) algorithm. Extensive experiments are conducted on four benchmark face databases: the Olivetti Research Laboratory (ORL) database, the Yale database, the AR database and the Face Recognition Technology (FERET) database, to evaluate the proposed method’s performance. Comparative results demonstrate that the WWCSC model maintains superior recognition rates even under challenging conditions involving significant occlusion or corruption, highlighting its remarkable robustness in face recognition tasks. This study provides both theoretical and empirical validation for the effectiveness of the proposed approach. Full article