Search Results (804)

Background: The knee joint performs a vital function in human movement, supporting significant loads and ensuring stability during daily activities. Methods: The objective of this study was to develop and validate a subject-specific framework to model knee flexion–extension by integrating 3D gait data with individualized musculoskeletal (MS) and finite element (FE) models. In this proof of concept, gait data were collected from a 52-year-old woman using Xsens inertial sensors. The MS model was based on the same subject to define realistic loading, while the 3D knee FE model, built from another individual’s MRI, included all major anatomical structures, as subject-specific morphing was not possible due to unavailable scans. Results: The FE simulation showed principal stresses from –28.67 to +44.95 MPa, with compressive stresses between 2 and 8 MPa predominating in the tibial plateaus, consistent with normal gait. In the ACL, peak stress of 1.45 MPa occurred near the femoral insertion, decreasing non-uniformly with a compressive dip around –3.0 MPa. Displacement reached 0.99 mm in the distal tibia and decreased proximally. ACL displacement ranged from 0.45 to 0.80 mm, following a non-linear pattern likely due to ligament geometry and local constraints. Conclusions: These results support the model’s ability to replicate realistic, patient-specific joint mechanics. Full article

Hierarchical surface structuring is a critical aspect of advanced materials design, impacting fields ranging from optics to biomimetics. Among several laser-based methods for complex structuring of photo-responsive surfaces, the broadband vectorial interferometry proposed here offers unique performances. Such a method leverages a polychromatic laser source, an unconventional choice for holographic encoding, to achieve deterministic multiscale surface structuring through interference light patterning. Azopolymer films are used as photosensitive substrates. By exploring the interaction between optomechanical stress modulations at different spatial periodicities induced within the polymer bulk, we demonstrate the emergence of hierarchical Fourier surfaces composed of multiple deterministic levels. These structures range from sub-micrometer to tens of micrometers scale, exhibiting a high degree of control over their morphology. The experimental findings reveal that the optical encoding scheme significantly influences the resulting topographies. The polarization light patterns lead to more regular and symmetric hierarchical structures compared to those obtained with intensity patterns, underscoring the role of vectorial light properties in controlling surface morphologies. The proposed method is fully scalable, compatible with more complex recording schemes (including multi-beam interference), and it is applicable to a wide range of advanced technological fields. These include optics and photonics (diffractive elements, polarimetric devices), biomimetic surfaces, topographical design, information encoding, and anti-counterfeiting, offering a rapid, reliable, and versatile strategy for high-precision surface structuring at a submicrometric scale. Full article

In this study, two color morphs (red and green) of Asian swimming crab (Charybdis japonica) commonly distributed in the China Sea area were analyzed for their L*a*b* values, carapace and inner membrane histology, morphological characteristics, mitochondrial DNA sequences, muscle texture, and amino acid composition. The results showed that compared with the green morph group, the red morph group exhibits higher aggregation of melanocytes and fewer pigment cells in the inner membrane. In addition, L* and b* of the carapace, and L* values of the inner membrane were lower in red morph group. Both populations of C. japonica also exhibit significant differences in their morphological parameters, including carapace length, body weight, and pincer width. However, the coefficient of variation for these morphological parameters did not correspond to the subspecies level. The mitochondrial DNA analysis also revealed sequence identity of COI (98.96%) and ITS-1 (99.71%) genes in both groups, supporting them to belong to the same species. Both groups also presented significant differences in their muscle texture characteristics, including adhesiveness, springiness, and gumminess, but no significant differences were observed in the muscle amino acid composition. Overall, red and green morphs of C. japonica show differences in their body color, morphological characteristics, and muscle quality, but still belong to the same species. Full article

Trajectory planning is crucial for solar aircraft endurance. The multi-wing morphing solar aircraft can enhance solar energy acquisition through wing deflection, which simultaneously incurs aerodynamic losses, complicating energy coupling and challenging existing planning methods in efficiency and long-term optimization. This study presents an energy-optimal trajectory planning method based on Hierarchical Reinforcement Learning for morphing solar-powered Unmanned Aerial Vehicles (UAVs), exemplified by a Λ-shaped aircraft. This method aims to train a hierarchical policy to autonomously track energy peaks. It features a top-level decision policy selecting appropriate bottom-level policies based on energy factors, which generate control commands such as thrust, attitude angles, and wing deflection angles. Shaped properly by reward functions and training conditions, the hierarchical policy can enable the UAV to adapt to changing flight conditions and achieve autonomous flight with energy maximization. Evaluated through 24 h simulation flights on the summer solstice, the results demonstrate that the hierarchical policy can appropriately switch its bottom-level policies during daytime and generate real-time control commands that satisfy optimal energy power requirements. Compared with the minimum energy consumption benchmark case, the proposed hierarchical policy achieved 0.98 h more of full-charge high-altitude cruise duration and 1.92% more remaining battery energy after 24 h, demonstrating superior energy optimization capabilities. In addition, the strong adaptability of the hierarchical policy to different quarterly dates was demonstrated through generalization ability testing. Full article

This encyclopedia entry provides an updated appreciation of the evolution of morphing aircraft wings, organized as follows: first, lift concepts are briefly examined; second, patents related to lift enhancement are discussed, showcasing existing technology and its evolution; finally, several technologies for morphing wings and the role of UAVs as testbeds for many innovative concepts are highlighted. The background of morphing wings is presented through a recap of lift concepts and the presentation of representative patents that describe the evolution of leading-edge and trailing-edge devices, such as flaps, slats, spoilers, and control surfaces. Although these topics are not usually detailed in reviews of morphing wings, they are deemed relevant for this encyclopedia entry. Full article

In recent times, blades that have the ability to change shape passively or actively have garnered interest due to their ability to optimize blade performance for varying flow conditions. Various versions of morphing exist, from simple chord length changes to full blade morphing with multiple degrees of freedom. These blades can incorporate smart materials or mechanical actuators to modify the blade shape to suit the wind conditions. Morphing blades have shown an ability to improve performance in simulations. These simulations show increased performance in Region 2 (partial load) operating conditions. This study focuses on the effects of the wake for a flexible wind turbine with actively variable twist angle distribution (TAD) to improve the energy production capabilities of morphing structures. These wake effects influence wind farm performance for locally clustered turbines by extracting energy from the free stream. Hence, the development of better wake models is critical for better turbine design and controls. This paper provides an outline of some approaches available for wake modeling. FLORIS (FLow Redirection and Induction Steady-State) is a program used to predict steady-state wake characteristics. Alongside that, the Materials and Methods section shows different modeling environments and their possible integration into FLORIS. The Results and Discussion section analyzes the 20 kW wind turbine with previously acquired data from the National Renewable Energy Laboratory’s (NREL) AeroDyn v13 software. The study employs FLORIS to simulate steady-state non-linear wake interactions for the nine TAD shapes. These TAD shapes are evaluated across Region 2 operating conditions. The previous study used a genetic algorithm to obtain nine TAD shapes that maximized aerodynamic efficiency in Region 2. The Results and Discussion section compares these TAD shapes to the original blade design regarding the wake characteristics. The project aims to enhance the understanding of FLORIS for studying wake characteristics for morphing blades. Full article

Nature’s principles offer design references for additive manufacturing (AM), enabling structures that achieve remarkable efficiency through hierarchical organization rather than material excess. This perspective article proposes a framework for integrating biomimetic principles into AM beyond morphological mimicry, focusing on functional adaptation and sustainability. By emulating biological systems like nacre, spider silk, and bone, AM utilizes traditional geometric replication to embed multifunctionality, responsiveness, and resource efficiency. Recent advances in the fields of 4D printing, soft robotics, and self-morphing systems demonstrate how time-dependent behaviors and environmental adaptability can be engineered through bioinspired material architectures. However, challenges in scalable fabrication, dynamic material programming, and true functional emulation (beyond morphological mimicry) necessitate interdisciplinary collaboration. In this context, the synthesis of biological intelligence with AM technologies offers sustainable, high-performance solutions for aerospace, biomedical, and smart infrastructure applications, once challenges related to material innovation and standardization are overcome. Full article

Bioinspired morphing offers a powerful route to higher aerodynamic and hydrodynamic efficiency. Birds reposition feathers, bats extend compliant membrane wings, and fish modulate fin stiffness, tailoring lift, drag, and thrust in real time. To capture these advantages, engineers are developing airfoils, rotor blades, and hydrofoils that actively change shape, reducing drag, improving maneuverability, and harvesting energy from unsteady flows. This review surveys over 296 studies, with primary emphasis on literature published between 2015 and 2025, distilling four biological archetypes—avian wing morphing, bat-wing elasticity, fish-fin compliance, and tubercled marine flippers—and tracing their translation into morphing aircraft, ornithopters, rotorcraft, unmanned aerial vehicles, and tidal or wave-energy converters. We compare experimental demonstrations and numerical simulations, identify consensus performance gains (up to 30% increase in lift-to-drag ratio, 4 dB noise reduction, and 15% boost in propulsive or power-capture efficiency), and analyze materials, actuation, control strategies, certification, and durability as the main barriers to deployment. Advances in multifunctional composites, electroactive polymers, and model-based adaptive control have moved prototypes from laboratory proof-of-concept toward field testing. Continued collaboration among biology, materials science, control engineering, and fluid dynamics is essential to unlock robust, scalable morphing technologies that meet future efficiency and sustainability targets. Full article

An aircraft that has been carefully optimized for a single flight condition will tend to perform poorly at other flight conditions. For aircraft such as long-haul airliners, this is not necessarily a problem, since the cruise condition so heavily dominates a typical mission. However, other aircraft, such as Unmanned Aerial Vehicles (UAVs), may be expected to perform well at a wide range of flight conditions. Morphing systems may be a solution to this problem, as they allow the aircraft to adapt its shape to produce optimum performance at each flight condition. This study proposes an aerodynamic optimization framework for morphing airfoils by integrating Principal Component Analysis (PCA) for geometric dimensionality reduction and deep learning (DL) for surrogate modeling, alongside an optimization-guided data augmentation strategy. By employing PCA, the geometric dimensionality of airfoil surfaces is reduced from 24 to 18 design variables while preserving 100% shape fidelity, thus establishing a compressed morphing parameterization space. A Multi-Island Genetic Algorithm (MIGA) efficiently explores the reduced design space, while iterative retraining of the surrogate model enhances prediction accuracy, particularly in high-performance regions. Additionally, Shapley Additive Explanation (SHAP) analysis reveals interpretable correlations between principal component modes and aerodynamic performances. Experimental results show that the optimized airfoil achieves a 54.66% increase in low-speed cruise lift-to-drag ratio and 10.90% higher climb lift compared to the baseline. Overall, the proposed framework not only enhances the adaptability of morphing airfoils across various low-speed flight conditions but also facilitates targeted surrogate refinement and efficient data acquisition in high-performance regions. Full article

Hypersonic morphing vehicles (HMVs), renowned for their adaptive structural reconfiguration and cross-domain maneuverability, confront formidable reentry guidance challenges under multiple no-fly zones, stringent path constraints, and nonlinear dynamics exacerbated by morphing-induced aerodynamic uncertainties. To address these issues, this study proposes a hierarchical framework integrating an A-based energy-optimal waypoint planner, a deep deterministic policy gradient (DDPG)-driven morphing policy network, and a quasi-equilibrium glide condition (QEGC) guidance law with continuous sliding mode control. The A* algorithm generates heuristic trajectories circumventing no-fly zones, reducing the evaluation function by 6.2% compared to greedy methods, while DDPG optimizes sweep angles to minimize velocity loss and terminal errors (0.09 km position, 0.01 m/s velocity). The QEGC law ensures robust longitudinal-lateral tracking via smooth hyperbolic tangent switching. Simulations demonstrate generalization across diverse targets (terminal errors < 0.24 km) and robustness under Monte Carlo deviations (0.263 ± 0.184 km range, −12.7 ± 42.93 m/s velocity). This work bridges global trajectory planning with real-time morphing adaptation, advancing intelligent HMV control. Future research will extend this framework to ascent/dive phases and optimize its computational efficiency for onboard deployment. Full article

E-commerce live streaming in China has become a major retail channel, yet hosts often employ subtle phonetic or semantic “morphs” to evade moderation and make unsubstantiated claims, posing risks to consumers. To address this, we study the Live Auditory Morph Resolution (LiveAMR) task, which restores morphed speech transcriptions to their true forms. Building on prior text-based morph resolution, we propose an LLM-enhanced training framework that mines three types of explanation knowledge—predefined morph-type labels, LLM-generated reference corrections, and natural-language rationales constrained for clarity and comprehensiveness—from a frozen large language model. These annotations are concatenated with the original morphed sentence and used to fine-tune a lightweight T5 model under a standard cross-entropy objective. In experiments on two test sets (in-domain and out-of-domain), our method achieves substantial gains over baselines, improving $F_{0.5}$ by up to 7 pp in-domain (to 0.943) and 5 pp out-of-domain (to 0.799) compared to a strong T5 baseline. These results demonstrate that structured LLM-derived signals can be mined without fine-tuning the LLM itself and injected into small models to yield efficient, accurate morph resolution. Full article

Recently, morphing quadcopters have gained an unprecedented popularity due to their nature of flexibility, self-controlled arm management and diversified application. It has been established that in morphing quadcopter control, aerial morphing generally introduces time-varying parameters into the dynamic model, thereby increasing the complexity of the control problem, in addition to the non-linearity, coupling dynamics, and external disturbances present in the model. Thus, to address those challenges, this research aimed at developing a robust backstepping sliding mode controller (BSMC) for morphing quadcopter position and orientation control. to achieve the stated aim, mathematical model of an active morphing quadcopter (a foldable drone) was presented considering five morphing formations (X, H, T, O, and Y). Following the development of the system model, the proposed control method was designed in two stages: a high-performance sliding mode controller (HSMC) for attitude control to ensure chattering-free and fast convergence of the orientation angles and a backstepping controller for position control. The robustness and effectiveness of the proposed controller were investigated and benchmarked against a backstepping control approach. The simulation results obtained show the effectiveness of the developed controller against the backstepping approach in the presence of parameter variations and external disturbances. Full article

This paper tackles the challenge of achieving robust and precise control for a novel quadrotor featuring continuously variable arm lengths (15 cm to 19 cm), enabling enhanced adaptability in complex environments. Unlike conventional fixed-geometry or discretely morphing unmanned aerial vehicles, this design’s continuous structural changes introduce significant complexities in modeling its time-varying moment of inertia. To address this, we propose a control strategy that decouples dynamic motion from the evolving geometry, allowing for the development of a robust control model. A sliding mode control algorithm, optimized using particle swarm optimization, is implemented to ensure stability and high performance in the presence of uncertainties and noise. Extensive MATLAB 2016 simulations validate the proposed approach, demonstrating superior tracking accuracy in both fixed and variable arm-length configurations, achieving root mean square error values of 0.05 m (fixed arms), 0.06 m (variable arms, path 1), and 0.03 m (variable arms, path 2). Notably, the PSO-tuned SMC controller reduces tracking error by 30% (0.07 m vs. 0.10 m for PID) and achieves a 40% faster settling time during structural transitions. This improvement is attributed to the PSO-optimized SMC parameters that effectively adapt to the continuously changing inertia, concurrently minimizing chattering by 10%. This research advances the field of morphing UAVs by integrating continuous geometric adaptability with precise and robust control, offering significant potential for energy-efficient flight and navigation in confined spaces, as well as applications in autonomous navigation and industrial inspection. Full article

The presented study demonstrates that UAVs can be flown with a morphing wing to develop essential aerodynamic efficiency without a tail structure, which decides the operational cost and flight safety. The mechanical control for morphing is discussed, where the system design, simulation, and experimental realization of ±15° SDOF sweep motion for a 7 kg eVTOL wing are detailed. The methodology, developed through a mathematical modeling of the mechanism’s kinematics and dynamics, is explained using Denavit–Hartenberg (D-H) convention, Lagrangian mechanics, and Euler–Lagrangian equations. The simulation and MBD analyses were performed in MATLAB R2021 and by Altair Motion Solve, respectively. The experiment was conducted on a dedicated test rig with two wing variants fitted with IMUs and an autopilot. The results from various methods were analyzed and experimentally compared to provide an accurate insight into the system’s design, modeling, and performance of the sweep morphing wing. The theoretical calculations by the mathematical model were compared with the test results. The sweep requirement is essential for eVTOL to have long endurance and multi-mission capabilities. Therefore, the developed sweep morphing mechanism is very useful, meeting such a demand. However, the results for three-dimensional morphing, operating sweep, pitch, and roll together are also presented, for the sake of completeness. Full article

Remote sensing precipitation products are essential for the systematic analysis of precipitation characteristics and changes. This study conducts a comparative evaluation of precipitation products from rain gauge stations, Climate Prediction Center morphing technique (CMORPH), Tropical Rainfall Measuring Mission precipitation radar (TRMM PR) version 7 and Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar Ku band (DPR KuPR) version 6 orbital observations during the common observational period (April–September 2014) across South China. The spatial patterns and probability density function of rain rates from four precipitation products show similar features. However, average rain rates from CMORPH (0.2–2.6 mm/h) tend to be smaller than those from rain gauge (0.1–4.4 mm/h) in temporal and spatial distribution. Conversely, average rain rates from TRMM PR and GPM KuPR (0.4–10.0 mm/h) are generally larger and exhibit more pronounced monthly changes. Despite notable differences in the number of detection samples, TRMM and GPM exhibit comparable spatiotemporal distributions and vertical structures, including rain-rate profiles, storm top heights and liquid (ice) water path. This confirms the consistency of space-borne precipitation radars and provides a foundation for analyzing long-term precipitation trends. Further analysis reveals that light rain rates from CMORPH have relatively small deviations, while rain rates generally tend to underestimate the rain rate compared to rain gauge. In contrast, TRMM PR and GPM KuPR tend to generally overestimate rain rates. Meanwhile, CMORPH (1.5–6.0 mm/h) shows larger deviations from rain gauge than TRMM and GPM, and the bias progressively increases as rain rates rise, as indicated by root mean square error results. Several statistical metrics suggest that although the missing detection rates of TRMM and GPM are higher than those of CMORPH (probability of detection 10–60%), their false detection rates are spatially lower (false alert ratio 10–30%) in Middle-East China. This study aims to provide valuable insights for enhancing precipitation retrieval algorithms and improving the applicability of remote sensing precipitation products. Full article