The stochastic dynamics of flagellar beating for micro-swimmers, such as flagellated cells, sperms and microalgae, is dominated by a feedback mechanism between flagellar shape and the rate of activation/de-activation of the $N \gg 1$ driving molecular motors. In the context of the so-called rigid filament models, where the axoneme is described by a single degree of freedom $X(t)$, we investigate the effect of direct coupling between the activity dynamics of adjacent motors, parametrized by $K \ge 0$. A functional Fokker-Planck equation for $X$ and the state of the $N$ motors is obtained. In the limit of small coupling $K \ll 1$, we derive a system of equations governing the dynamics of the Fourier modes of the active motor density, obtaining estimates for several observables and the fluctuations' quality factor $Q$. For larger $K$ we resort to numerical simulations. The effect of introducing the coupling $K>0$ is to increase characteristic times and the beating period. Moreover at large $K$s the limit cycle becomes bi-stable, with abrupt avalanches of the motor dynamics. Increasing $K$ is similar to what observed in the case $K=0$ when the confining elastic force is strongly reduced. The quality factor of fluctuations has a non-monotonic behavior: it first increases with $K$, then decreases. This is accompanied by the reduction and eventual disappearance of regions where the fraction of activated motor is nor $0$ neither $1$.
Flagellar motors enable bacteria to navigate their environments by switching rotation direction in response to external cues with high sensitivity. Previous work suggested that ultrasensitivity of the flagellar motor originates from conformational spread, in which subunits of the switching complex are strongly coupled to their neighbors as in an equilibrium Ising model. However, dynamic single-motor measurements indicated that rotation switching is driven out of equilibrium, and the mechanism for this dissipative driving remains unknown. Here, based on recent cryo-EM structures, we propose that local mechanical torques on motor subunits can affect their conformation dynamics. This gives rise to a tug of war between stator-associated subunits, which produces cooperative, non-equilibrium switching responses without requiring nearest-neighbor interactions. Since subunits are effectively coupled at a distance, we call this mechanism ``Global Mechanical Coupling." Our model makes a qualitatively new prediction that the motor response cooperativity grows with the number of stators driving rotation. Re-analyzing published motor dose-response curves in varying load conditions, we find tentative experimental evidence for this prediction. Finally, we show that operating out of equilibrium enables motors to achieve high cooperativity with faster responses compared to equilibrium motors. Our results suggest a general role for mechanics in sensitive chemical regulation.
Ricco C. Venterea, John Orlowski-Scherer, Nicholas Battaglia
et al.
We present A databaSe of millimeTeR ObservatioNs of Asteroids Using acT (ASTRONAUT) hosted on Amazon Web Services, Inc. (AWS) in the form of a public Amazon Simple Storage Service (S3) bucket. This bucket is an Amazon cloud storage database containing flux measurements for a group of asteroids at millimeter (mm) wavelengths. These measurements were collected by the Atacama Cosmology Telescope (ACT) from 2017 to 2021 in frequency bands centered near 90, 150, and 220 GHz. The ASTRONAUT database contains observation times, normalized flux values, and associated error bars for 170 asteroids above a signal-to-noise ratio of 5 for a single frequency band over the stacked co-added maps. We provide an example in generating light curves with this database. We also present a Jupyter notebook to serve as a reference guide when using the S3 bucket. The container and notebook are publicly available in a GitHub repository.
Alessandro Colombo, Riccardo Busetto, Valentina Breschi
et al.
Accurate speed estimation in sensorless brushless DC motors is essential for high-performance control and monitoring, yet conventional model-based approaches struggle with system nonlinearities and parameter uncertainties. In this work, we propose an in-context learning framework leveraging transformer-based models to perform zero-shot speed estimation using only electrical measurements. By training the filter offline on simulated motor trajectories, we enable real-time inference on unseen real motors without retraining, eliminating the need for explicit system identification while retaining adaptability to varying operating conditions. Experimental results demonstrate that our method outperforms traditional Kalman filter-based estimators, especially in low-speed regimes that are crucial during motor startup.
Aluminum/lithium (Al/Li) alloy is a promising energetic material for solid composite propellants. The bonding structure, topological shape, density, cohesive energy, and mechanical and diffusion properties of the Al/Li alloy bulks and oxidation shells are calculated systematically using the large-scale force-field molecular dynamics simulations together with the ab initio quantum chemistry calculations. Theoretical predicted structures and dynamic properties for various crystalline and amorphous reference compounds are compared with the available experimental data to validate the force-field simulations. The dependence of the structures and properties on the Li contents ranging from 2 to 50 wt% is clarified. It is revealed that both Al and Li atoms are resident in the same Al or Li environment in the Al/Li alloys. The presence of the crystalline δ’-Al<sub>3</sub>Li and β-AlLi phases in the Al/Li alloys is rationalized in terms of the coordination of Al/Li and the thermodynamic free energy of Li substitution. A homogenous six-coordinated Al/Li alloy could be generated with a Li content of 20 wt%. Young’s moduli of the alloys are improved via the low Li addition due to the anisotropic effect. The Al/Li/O oxidation shell is less dense than the amorphous alumina but the densities of oxides are generally higher than those of the corresponding Al/Li alloys. As the Li content increases, the Al/Li/O oxides form the ordered four-coordinated AlO<sub>4</sub> passages together with the under-coordinated Li-O units, leading to considerably deteriorated mechanical performance and efficient Li diffusion with an activation energy of about 20 kJ/mol. The present work provides a deep understanding of the Al/Li alloys and Al/Li/O oxides in terms of performance and exposure stability.
Cross-domain authentication of drones has played an important role in emergency rescue, collaborative missions, and so on. However, the existing cross-domain authentication protocols for drones may cause privacy leakages and stolen-verifier attacks due to the storage of drone information by ground stations, and drones and ground stations are susceptible to capture attacks, which may suffer from impersonation attacks. To address these problems, we propose a lightweight cross-domain authentication protocol based on physical unclonable function (PUF). In the proposed protocol, the control center is not involved in the authentication process, preventing bottleneck problems when multiple drones authenticate simultaneously. Ground stations do not store drone information, effectively safeguarding against privacy leakage and stolen-verifier attacks. PUF is utilized to protect drones from capture attacks. We conduct both informal security analysis and formal security proof to demonstrate the protocol’s security. In terms of performance, compared with relevant schemes, our protocol shows remarkable efficiency improvements. Computationally, it is 5–92% more efficient. Regarding communication overhead, it is 9–68% lower than relevant schemes. For storage, it is 22–48% lower than relevant schemes. We simulated the proposed protocol using a Raspberry Pi 4B, which emulates the computational capabilities of actual UAV and ground stations. During the simulation, a large number of authentication requests were generated. We monitored key performance indicators such as authentication success rate, response time, and resource utilization. To test its security, we simulated common attacks like replay, forgery, and impersonation. The protocol’s timestamps effectively identified and rejected replayed messages. Meanwhile, the PUF mechanism and unique signature scheme foiled our attempts to forge authentication messages. These simulation results, combined with theoretical security proofs, confirm the protocol’s practical viability and security in real-world-like scenarios.
Addressing lateral-directional control challenges during unmanned aerial vehicle (UAV) landing in complex wind fields, this study proposes a drift angle control strategy that integrates coordinated heading and trajectory regulation. An adaptive radius optimization method for the Dubins approach path is designed using wind speed estimation. By developing a wind-coupled flight dynamics model, we establish a roll angle control loop combining the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>L</mi><mn>1</mn></msub></mrow></semantics></math></inline-formula> nonlinear guidance law with Linear Active Disturbance Rejection Control (LADRC). Simulation tests against conventional sideslip approach and crab approach, along with flight tests, confirm that the proposed autonomous landing system achieves smoother attitude transitions during landing while meeting all touchdown performance requirements. This solution provides a theoretically rigorous and practically viable approach for safe UAV landings in challenging wind conditions.
Based on the roller thread equation, the spur tooth profile equation, and coordinate transformation relationships, the surface equations of the incomplete gear pairs in the planetary roller screw mechanism were established. Combining the gear meshing principle with the surface equations of incomplete gear pairs, a model for calculating the contact point positions and contact line lengths of incomplete gear pairs was proposed. A finite element model for incomplete gear pair was introduced to validate the correctness of the present model. The influence of the initial angle and width of the spur gear on the meshing characteristics of incomplete gear pairs was analyzed. The results show that under the same meshing radius, the maximum and minimum contact line lengths on each spur tooth structure are not affected by the initial angles of the roller's spur tooth profile and the roller's spur thread. The analysis shows that when the tooth width of the incomplete gear pair is an integer multiple of the roller thread lead, under the same meshing radius, the contact line lengths on any spur tooth structure are identical.
Airport airspace safety is increasingly threatened by small, unmanned aircraft systems and wildlife that traditional radar cannot detect. While earlier reviews addressed general counter-UAS techniques, individual sensors, or the detection of single objects such as birds or drones, none has systematically reviewed airport-based, multi-sensor surveillance strategies through a safety-theoretical lens. A systematic review, performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement, synthesized recent research on fixed, ground-based aerial detection capabilities for small aerial hazards, specifically unmanned aircraft systems (sUAS) and avian targets, within operational airport environments. Searches targeted English-language, peer-reviewed articles from 2016 through 2025 in Web of Science and Scopus. Due to methodological heterogeneity across sensor technologies, a narrative synthesis was executed. The review of thirty-six studies, analyzed through Reason’s Swiss Cheese Model and Endsley’s Situational Awareness framework, found that only layered multi-sensor fusion architectures effectively address detection gaps for Low-Slow-Small (LSS) threats. Based on these findings, the review proposes seamless integration with Air Traffic Management (ATM) and UAS Traffic Management (UTM) systems through standardized data-exchange interfaces, complemented by theoretically grounded risk-based deployment strategies aligning surveillance technology tiers with operational risk profiles, from basic Remote ID receivers in low-risk rural environments to comprehensive multi-sensor fusion at high-density hubs, major airports, and urban vertiports.
Sugiri Sugiri, Mochamad Bruri Triyono, Yosef Budiman
et al.
Modern automotive design has increasingly embraced plastics for bumper construction; however, it can lead to material degradation. To overcome these limitations, the automotive industry is turning to fiber–resin material, namely carbon–epoxy composites. Our research focuses on determining the effects of fiber orientation and angle alignment on the structural stress of the car bumper, examining the hybrid material (carbon–epoxy reinforced by CFRP) in static structural tests, and performing dynamic impact tests at various speeds, applying the Tsai–Wu criterion as a basic failure model. However, Tsai–Wu’s failure in numerical analysis highlights the limitation of not being able to experimentally distinguish between failure modes and their interaction coefficients. To address this issue, we employ ANSYS<sup>®</sup> 2024 R1 with a Fortran program, which enables more accurate estimation of failure behavior, resulting in an average error of 13.19%. To identify research gaps, machine learning (ML) plays a vital role in predicting parameter values and assessing data normality using various algorithms. By combining ML and FEA simulations, the result shows strong data performance. Bridging from 2 mm mesh sizing of 50% carbon–epoxy woven/50% CFRP laminate in 6 mm thickness at 0° orientation shows the most distributed shear stresses and deformation, which converged toward stable values. For comprehensive research, total deformation was included in ML analysis as a second target to build a multivariate analysis. Overall, Random Forest (RF) is the best-performing model, indicating superior robustness for modeling shear stress and total deformation.
Mechanical engineering and machinery, Machine design and drawing
To achieve real-time phase detection, this paper presents a fast and precise spatial carrier phase-shifting interferometry based on the dynamic mode decomposition strategy. The algorithm initially produces a series of phase-shifted sub-interferograms with the aid of a spatial carrier interferogram. Subsequently, the measured phases are derived with great accuracy from these sub-interferograms through the use of the dynamic mode decomposition strategy, an outstanding non-iterative algorithm. Numerical simulation and experimental comparison show that this method is an efficient and accurate single-frame phase demodulation algorithm. The paper also analyzes the performance of the proposed method based on influencing factors such as random noise level, carrier frequency size, and carrier frequency direction. The results indicate that this method is a fast and accurate phase solution method, offering another effective solution for dynamic real-time phase measurement.
Glioma cells can reshape functional neuronal networks by hijacking neuronal synapses, leading to partial or complete neurological dysfunction. These mechanisms have been previously explored for language functions. However, the impact of glioma on sensorimotor functions is still unknown. Therefore, we recruited a control group of patients with unaffected motor cortex and a group of patients with glioma-infiltrated motor cortex, and recorded high-density electrocortical signals during finger movement tasks. The results showed that glioma suppresses task-related synchronization in the high-gamma band and reduces the power across all frequency bands. The resulting atypical motor information transmission model with discrete signaling pathways and delayed responses disrupts the stability of neuronal encoding patterns for finger movement kinematics across various temporal-spatial scales. These findings demonstrate that gliomas functionally invade neural circuits within the motor cortex. This result advances our understanding of motor function processing in chronic disease states, which is important to advance the surgical strategies and neurorehabilitation approaches for patients with malignant gliomas.
Accurate and comprehensive 3D sensing using LiDAR systems is crucial for various applications in photogrammetry and robotics, including facility inspection, Building Information Modeling (BIM), and robot navigation. Motorized LiDAR systems can expand the Field of View (FoV) without adding multiple scanners, but existing motorized LiDAR systems often rely on constant-speed motor control, leading to suboptimal performance in complex environments. To address this, we propose UA-MPC, an uncertainty-aware motor control strategy that balances scanning accuracy and efficiency. By predicting discrete observabilities of LiDAR Odometry (LO) through ray tracing and modeling their distribution with a surrogate function, UA-MPC efficiently optimizes motor speed control according to different scenes. Additionally, we develop a ROS-based realistic simulation environment for motorized LiDAR systems, enabling the evaluation of control strategies across diverse scenarios. Extensive experiments, conducted on both simulated and real-world scenarios, demonstrate that our method significantly improves odometry accuracy while preserving the scanning efficiency of motorized LiDAR systems. Specifically, it achieves over a 60\% reduction in positioning error with less than a 2\% decrease in efficiency compared to constant-speed control, offering a smarter and more effective solution for active 3D sensing tasks. The simulation environment for control motorized LiDAR is open-sourced at: \url{https://github.com/kafeiyin00/UA-MPC.git}.
A new three-phase hybrid-excited multi-tooth switched reluctance motor with embedded permanent magnets is proposed, capable of achieving higher torque density for transportation electrification applications. Operating principles and design considerations are discussed. A magnetic equivalent circuit is developed. Finite element method is employed in the field analysis. The advantages of the proposed topology over existing designs for switched reluctance motors and flux switching motors are presented. Finally, the optimized design is prototyped to experimentally confirm the design and simulation results.
Work on trajectory optimization is evolving rapidly due to the introduction of Artificial-Intelligence (AI)-based algorithms. Small UAVs are expected to execute versatile maneuvers in unknown environments. Prior studies on these UAVs have focused on conventional controller design, modeling, and performance, which have posed various challenges. However, a less explored area is the usage of reinforcement-learning algorithms for performing agile maneuvers like transition from hover to cruise. This paper introduces a unified framework for the development and optimization of a tilt-rotor tricopter UAV capable of performing Vertical Takeoff and Landing (VTOL) and efficient hover-to-cruise transitions. The UAV is equipped with a reinforcement-learning-based control system, specifically utilizing algorithms such as Deep Deterministic Policy Gradient (DDPG), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). Through extensive simulations, the study identifies PPO as the most robust algorithm, achieving superior performance in terms of stability and convergence compared with DDPG and TRPO. The findings demonstrate the efficacy of DRL in leveraging the unique dynamics of tilt-rotor UAVs and show a significant improvement in maneuvering precision and control adaptability. This study demonstrates the potential of reinforcement-learning algorithms in advancing autonomous UAV operations by bridging the gap between dynamic modeling and intelligent control strategies, underscoring the practical benefits of DRL in aerial robotics.
The harsh environment during airplane take-off and flights with complex operating conditions require a high dynamic and impact resistance capability of airplane engines. The design, development, and performance evaluation of new turbofan engines are generally performed through numerical simulations before a full-scale model or prototype experiment for certification. Simulations of fan blade containment tests can reduce trial–error testing and are currently the most convenient and inexpensive alternative for design; however, certification failure is always a risk if the calibration of material models is not correctly applied. This work presents a three-dimensional computational model of a turbofan for designing new engines that meet the certification requirements under the blade containment test. Two calibrated Johnson–Cook plasticity and damage laws for Ti64 are assessed in a simulation of a turbofan blade containment test, demonstrating the ability of the models to be used in the safe design of aircraft engine components subjected to dynamic impact loads with large deformations and adequate damage tolerance.
Unmanned aerial vehicles (UAVs) provide benefits through eco-friendliness, cost-effectiveness, and reduction of human risk. Deep reinforcement learning (DRL) is widely used for autonomous UAV navigation; however, current techniques often oversimplify the environment or impose movement restrictions. Additionally, most vision-based systems lack precise depth perception, while range finders provide a limited environmental overview, and LiDAR is energy-intensive. To address these challenges, this paper proposes VizNav, a modular DRL-based framework for autonomous UAV navigation in dynamic 3D environments without imposing conventional mobility constraints. VizNav incorporates the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with Prioritized Experience Replay and Importance Sampling (PER) to improve performance in continuous action spaces and mitigate overestimations. Additionally, VizNav employs depth map images (DMIs) to enhance visual navigation by accurately estimating objects’ depth information, thereby improving obstacle avoidance. Empirical results show that VizNav, by leveraging TD3, improves navigation, and the inclusion of PER and DMI further boosts performance. Furthermore, the deployment of VizNav across various experimental settings confirms its flexibility and adaptability. The framework’s architecture separates the agent’s learning from the training process, facilitating integration with various DRL algorithms, simulation environments, and reward functions. This modularity creates a potential to influence RL simulation in various autonomous navigation systems, including robotics control and autonomous vehicles.
This paper presents a new design for a multimode fusion communication system, aimed at tackling the complexities of modern aeronautical communication. The system integrates multiple communication technologies, such as ad hoc networking, 5G, BeiDou satellite, RTK positioning, and ADS-B broadcasting. This integration effectively solves the problem of increasing the size and weight of aviation communication equipment while also improving the efficiency and security of data communication. The study demonstrates that the implementation of this fusion communication system can lead to the development of more efficient and intelligent avionics equipment in the future, thereby offering robust technical support for flight safety.
Mikhail Yu. Mikheev, Oleg V. Prokofiev, Aleksandr E. Savochkin
et al.
Background. Over the past few years, there has been a dramatic expansion in the capabilities of artificial intelligence (AI) systems, which has simultaneously led to new risks and potential benefits. The autonomy of systems, which allows moving from decision support to self-development of a decision, provides conflicting results in areas of responsible application. Such sensitive areas of application as human health, its ecological living environment, economic and social status. In the field of armaments, the means of creating autonomous systems of a new generation and the concept of a future «hyperwar» associated with them are discussed. Materials and methods. Due to the growing use of AI around the world in these sensitive areas, there is a demand for reliability when using such autonomous systems. It is necessary to formulate the risks and benefits of this technology, including compliance with fundamental ethical principles. The application of critical decisions must be controlled by the person in charge. Measures to ensure the reliability of an autonomous system with AI must be provided at all stages of the life cycle, and only in this way it is possible to control risks and create explainable and manageable AI. Results and conclusions. The authors outlined the concept of the concept of «reliable» AI and described the implementation of its individual provisions at the stages of the life cycle of an autonomous system for responsible purposes.