Coordinating multi-articulated bodies to generate purposeful movement is a formidable computational challenge. Yet the human motor system performs this task robustly in dynamic, uncertain environments, despite noisy and delayed feedback, slow actuators, and strict energetic constraints. A central question is what organizational principles underlie this efficiency. One widely recognized principle of neural organization is modularity, which enables complex problems to be decomposed into simpler subproblems that specialized modules are optimized to solve. In this review, we argue that modularity is a fundamental organizing principle of the motor system. We first summarize evidence for brain modularity, ranging from classical lesion studies to contemporary graph-theoretical analyses. We next discuss the main factors underlying the emergence and evolutionary selection of modular architectures, highlighting the computational advantages they provide. We then review the major neuroanatomical modules that structure current descriptions of the motor system and compare three prominent computational frameworks of motor control$-$optimal feedback control theory, muscle synergy theory, and dynamical systems approaches$-$showing that all implicitly or explicitly rely on specialized computational modules. We conclude by contrasting the key strengths and limitations of existing frameworks and by proposing promising directions toward more comprehensive theories.
Utilizing coordinated UAV formations for emergency disaster relief is a key future trend, but traditional evaluation methods have three major drawbacks: high computational complexity, heavy reliance on expert experience, and poor generalization in multi-scenario small-sample settings. To address these issues, this paper first designs a four-level evaluation index system that covers 5 core capabilities and targets 4 typical disaster relief scenarios. Next, it establishes an AHP model that quantifies the performance of 406 UAV formations, thereby providing high-quality labeled data for subsequent research. Furthermore, the paper constructs a ResNet + Atten deep learning network with a hybrid architecture, which improves both the self-learning ability of expert knowledge and the efficiency of multi-scenario evaluation. To solve small-sample overfitting and expert bias, the paper proposes a physically meaningful controllable perturbation data augmentation method: one that works by perturbing 23 UAV performance metrics within a 5–15% range to expand the sample size. Comparative experiments are conducted using three methods, BP neural networks, ResNet, and LSTM, and results show that ResNet + Atten achieves superior performance. Additionally, the data augmentation method effectively enhances the generalization ability of the model. The proposed method provides a reliable method for evaluating the performance of UAV multi-scenario collaborative disaster relief operations.
Accurate cropland classification within smallholder farming systems is essential for effective land management, efficient resource allocation, and informed agricultural decision-making. This study evaluates cropland classification performance using Red, Green, Blue (RGB) and multi-spectral (blue, green, red, red-edge, near-infrared) unmanned aerial vehicle (UAV) imagery. Both datasets were derived from imagery acquired using a MicaSense Altum sensor mounted on a DJI Matrice 300 UAV. Cropland classification was performed using machine learning algorithms implemented within the Google Earth Engine (GEE) platform, applying both a non-binary classification of five land cover classes and a binary classification within a probabilistic framework to distinguishing cropland from non-cropland areas. The results indicate that multi-spectral imagery achieved higher classification accuracy than RGB imagery for non-binary classification, with overall accuracies of 75% and 68%, respectively. For binary cropland classification, RGB imagery achieved an area under the receiver operating characteristic curve (AUC–ROC) of 0.75, compared to 0.77 for multi-spectral imagery. These findings suggest that, while multi-spectral data provides improved classification performance, RGB imagery can achieve comparable accuracy for fundamental cropland delineation. This study contributes baseline evidence on the relative performance of RGB and multi-spectral UAV imagery for cropland mapping in heterogeneous smallholder farming landscapes and supports further investigation of RGB-based approaches in resource-constrained agricultural contexts.
We study the dynamics of molecular motor-driven transport into dendritic spines, which are bulbous intracellular compartments in neurons that play a key role in transmitting signals between neurons. We further develop a stochastic model of vesicle transport in [Park, Singh, and Fai, SIAM J. Appl. Math. 82.3 (2022), pp. 793--820] by showing that second-order moments may be neglected. We exploit this property to significantly simplify the model and confirm through numerical simulations that the simplification retains key behaviors of the original agent-based myosin model of vesicle transport. We use the simplified model to explore the vesicle translocation time and probability through dendritic spines as a function of molecular motor parameters, which was previously not practically possible. Relevance to Life Sciences: We find that thinner dendritic spine geometry can greatly reduce the probability of vesicle translocation to the post-synaptic density. The cell may alter molecular motor parameters to compensate, but only to a point. These findings are consistent with the biological literature, where brain disorders are often associated with an excess of long, thin dendritic spines. Mathematical Content: We use a moment-generating function to deduce that second-order moments in motor attachment times may be neglected, and therefore the first-order moment is a sufficient approximation. Using only the mean attachment times and neglecting the variance yields a tractable master equation from which vesicle mean first passage times may be computed directly as a function of geometry and molecular motor parameters.
Last-mile delivery (LMD) is an important aspect of contemporary logistics that directly affects operational cost, efficiency, and customer satisfaction. In this paper, we provide a review of the optimization techniques of LMD, focusing on Artificial Intelligence (AI) driven decision-making, IoT-supported real-time monitoring, and hybrid delivery networks. The combination of AI and IoT improves predictive analytics, dynamic routing, and fleet management, but scalability and regulatory issues are still major concerns. Hybrid frameworks that integrate drones or Unmanned Aerial Vehicles (UAVs), ground robots, and conventional vehicles reduce energy expenditure and increase delivery range, especially in urban contexts. Furthermore, sustainable logistics approaches, including electric vehicle fleets and shared delivery infrastructures, provide promise for minimizing environmental impact. However, economic viability, legal frameworks, and infrastructure readiness still influence the feasibility of large-scale adoption. This review offers a perspective on the changing patterns in LMD, calling for regulatory evolution, technological advancement, as well as interdisciplinary approaches toward cost-effective, durable, and environmentally friendly logistics systems.
Given the limited ground infrastructure in remote areas such as suburbs and rural regions, Internet of Remote Things (IoRT) devices have been widely deployed to gather critical data and information. However, traditional data transmission methods struggle to directly transmit the data to data processing centers. This paper proposes a space–air–ground Internet of Remote Things (SAG-IoRT) network architecture, which leverages the extensive coverage and efficient communication capabilities of satellites as its core advantage. In the SAG-IoRT network, low Earth orbit (LEO) satellites play a crucial role, addressing communication challenges in remote areas through their global coverage. Unmanned aerial vehicles (UAVs) serve as flexible bridges between ground and space, rapidly transmitting data collected by IoRT devices to LEO satellites, thereby enhancing data transmission efficiency and reliability. Our research focuses on optimizing the flight trajectories and scheduling strategies of UAVs to maximize the utilization of satellite communication resources, aiming to boost system throughput and reduce UAV energy consumption. To tackle the challenges of data collection and transmission in the dynamic and uncertain SAG-IoRT network environment, we formulate the optimization problem as a Markov decision process and apply the multi-agent deep deterministic policy gradient (MADDPG) algorithm to plan optimal paths for UAVs. Experimental results show that compared to the single-agent DDPG algorithm, the MADDPG-based solution not only improves system throughput by approximately 25.6% but also reduces UAV energy consumption by around 24.9%. This achievement underscores the pivotal role of satellites in advancing the development of IoRT and enabling efficient space-based communications.
Motor vehicles. Aeronautics. Astronautics, Astronomy
The vigorous development of urban air mobility (UAM) is reshaping the urban travel landscape, but it also poses severe challenges to the safe and efficient operation of dense and complex airspace. Potential conflicts between flight plans have become a core bottleneck restricting its development. Traditional flight plan adjustment and management methods often rely on deterministic trajectory predictions, ignoring the inherent temporal uncertainties in actual operations, which may lead to the underestimation of potential risks. Meanwhile, existing global optimization strategies often face issues of inefficiency and overly broad adjustment scopes when dealing with large-scale plan conflicts. To address these challenges, this study proposes an innovative flight plan conflict management framework. First, by introducing a probabilistic model of flight time errors, a new conflict detection mechanism based on confidence intervals is constructed, significantly enhancing the ability to foresee non-obvious conflict risks. Furthermore, based on complex network theory, the framework accurately identifies a small number of “critical flight plans” that play a core role in the conflict network, revealing their key impact on chain reactions of conflicts. On this basis, a phased optimization strategy is adopted, prioritizing the adjustment of spatiotemporal parameters (departure time and speed) for these critical plans to systematically resolve most conflicts. Subsequently, only fine-tuning the speeds of non-critical plans is required to address remaining local conflicts, thereby minimizing interference with the overall operational order. Simulation results demonstrate that this framework not only significantly improves the comprehensiveness of conflict detection but also effectively reduces the total number of conflicts. Additionally, the proposed phased artificial lemming algorithm (ALA) outperforms traditional optimization algorithms in terms of solution quality. This work provides an important theoretical foundation and a practically valuable solution for developing robust and efficient UAM dynamic scheduling systems, holding promise to support the safe and orderly operation of large-scale urban air traffic in the future.
A mathematical model was established to describe the sublimation and diffusion of water molecules and their adsorption onto cold traps. This model was used to analyze the combined influence mechanisms of sublimation temperature and ambient pressure on the vapor deposition process of water ice. Tunable Diode Laser Absorption Spectroscopy (TDLAS) was employed to provide real-time feedback on water vapor concentration within the experimental apparatus. Based on this feedback, the sublimation temperature was dynamically adjusted to maintain the concentration dynamically stabilized around the target value. A dedicated apparatus for generating controlled water vapor flow fields and detecting concentration was constructed. The accuracy of both the mathematical model and Finite Element Analysis (FEA) simulations was verified through comparative experiments. Laser triangulation was utilized as a method to detect the thickness of the adsorbed ice film on the sample surface. Leveraging this technique, a water vapor deposition and adsorption verification system was developed. This system was used to test the differences in water adsorption performance across various materials and to measure the correlation between the thickness of the adsorbed/deposited ice film on the samples and both deposition time and sublimation temperature.
Background. The paper discusses methods and strategies for improving the manufacturability of special equipment products. Materials and methods. Key aspects of design and production are analyzed, including component selection, mechanical installation, automation of control and adjustment operations, as well as the use of advanced shaping methods. Special attention is paid to improving product characteristics to increase efficiency, reduce costs and improve the quality of the final product. Results and conclusions. The results of the study emphasize the importance of integrating modern technologies and approaches to achieve market competitiveness.
Wear of the self-lubricating liner in aviation spherical plain bearings leads to increased clearance between the inner and outer rings, causing precision degradation of the control mechanism, thereby seriously threatening flight safety. Acoustic emission (AE)-based condition monitoring possesses many advantages such as high sensitivity, early-warning capability, and strong anti-interference performance, which can effectively address the limitation of traditional inspection methods in their inability to real-time characterize bearing operational states. First, a wear test setup with multiaxial loading capability for self-lubricating bearings and a split-type bearing base integrated with embedded AE sensors were designed, ensuring the fidelity of AE signal acquisition. Then, the evolutionary laws of AE signals from self-lubricating bearings in both time and frequency domains were studied, revealing the dual dominant frequency characteristics of AE signals and their dynamic energy evolution mechanism. A method for constructing bearing wear index based on acoustic emission time-frequency features and the multi-criteria feature selection strategy was proposed, enabling real-time assessment of the wear condition of self-lubricating bearings via wear index curves. It was found that the dynamic equilibrium of the PTFE transfer film in the bearing liner influences the energy distribution of AE frequency components. The critical transition interval for the test bearings between the running-in phase and stable wear phase was identified as 9 747 to 10 773 revolutions; and it was determined that the test bearings ultimately remained in the stable wear phase. Finally, the accuracy of the proposed wear index-based condition prediction method was validated using torque data. Furthermore, the micro-wear characteristics of the bearings were characterized through scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). The migration behavior of the PTFE transfer film and the interfacial chemical composition under the final wear state demonstrated the consistency of the wear phase determination.
The large cutout structure is a key component in the bottom skin of an airplane wing, and is susceptible to developing fatigue cracks under service loads. Early fatigue crack detection is crucial to ensure structural safety and reduce maintenance costs. Nonlinear Lamb wave techniques show significant potential in microcrack monitoring. However, nonlinear components are often relatively weak. In addition, a large cutout structure introduces complex boundary conditions for Lamb wave propagation, making nonlinear Lamb wave monitoring more challenging. This article proposes an integrated data processing method, combining phase inversion with continuous wavelet transform (CWT) to enhance crack detection in complex structures, with phase-velocity desynchronization adopted to suppress the material nonlinearity. Experiments on a large cutout aluminum alloy plate with thickness variations were conducted to validate the proposed method, and the results demonstrated its effectiveness in detecting fatigue cracks. Furthermore, this study found that nonlinear components are more effective than linear components in monitoring closed cracks.
Chemical gradients provide the primordial energy for biological functions by driving the mechanical movement of microscopic engines. Their thermodynamic properties remain elusive, especially concerning the dynamic change in energy demand in biological systems. In this article, we derive a constraint relation between the output power and the conversion efficiency for a chemically fueled steady-state rotary motor analogous to the $\mathrm{F}_o$ motor of ATPase. We find that the efficiency at maximum power is half of the maximum quasi static efficiency. These findings shall aid in the understanding of natural chemical engines and inspire the manual design and control of chemically fueled microscale engines.
This study addresses an under-represented topic in turbojets’ design—the characterizing of this type of engine as an entity subject to automatic control. This study’s subject is a medium-size turbojet, improved with a water injection system for thrust augmentation, and evaluated as a controlled object. The method of coolant injection in the compressor and/or in the combustion chamber of the aviation engine has been intensively studied and applied for the temporary increase in thrust. After a period of abandonment, the method seems to be returning in a version that also produces a reduction in pollutant emissions. Starting from determining turbojet performances on the test rig and establishing the equations that define the turbojet as a system, the mathematical model for both versions (basic and with a water injection) was issued. In order to correlate the basic engine operation with the water injection, a version of control architecture was designed, containing two controllers (for engine’s speed and for the injected water flow rate). An embedded control system was described by its mathematical model; based on its equations, its block diagram with transfer functions was issued. The system’s quality was evaluated by performing studies that concern the turbojet’s main parameters (speed and combustor temperature) and time behavior (system response at step input), which led to some results and conclusions regarding how the water injection changed the properties of the engine as a controlled object: the engine has become slower with bigger static errors for the studied parameters (affecting the stabilization at their values imposed by the new operating regime). The proposed method, based on the characterization of the engine as a controlled object (with and without coolant injection), can be very useful as a method of predicting the behavior of any turbojet when the addition of coolant injection system is desired; obviously, the appropriate modeling of both the turbojet and the injection system is necessary.
In this paper, an incremental nonlinear dynamic inversion (INDI) control scheme is proposed for the attitude tracking of a helicopter with model uncertainties, and actuator delay and saturation constraints. A finite integral compensation based on model reduction is used to compensate the actuator delay, and the proposed scheme can guarantee the semi-globally uniformly ultimately bounded tracking. The overall attitude controller is separated into a rate, an attitude, and a collective pitch controller. The rate and collective pitch controllers combine the proposed method and INDI to enhance the robustness to actuator delay and model uncertainties. Considering the dynamic of physical actuators, pseudo-control hedging (PCH) is introduced both in the rate and attitude controller to improve tracking performance. By using the proposed controller, the helicopter shows good dynamics under the multiple restrictions of the actuators.
Frailty is a geriatric syndrome associated with the lack of physiological reserve and consequent adverse outcomes (therapy complications and death) in older adults. Recent research has shown associations between heart rate (HR) dynamics (HR changes during physical activity) with frailty. The goal of the present study was to determine the effect of frailty on the interconnection between motor and cardiac systems during a localized upper-extremity function (UEF) test. Fifty-six older adults aged 65 or older were recruited and performed the UEF task of rapid elbow flexion for 20-seconds with the right arm. Frailty was assessed using the Fried phenotype. Wearable gyroscopes and electrocardiography were used to measure motor function and HR dynamics. Using convergent cross-mapping (CCM) the interconnection between motor (angular displacement) and cardiac (HR) performance was assessed. A significantly weaker interconnection was observed among pre-frail and frail participants compared to non-frail individuals (p<0.01, effect size=0.81$\pm$0.08). Using logistic models pre-frailty and frailty were identified with sensitivity and specificity of 82% to 89%, using motor, HR dynamics, and interconnection parameters. Findings suggested a strong association between cardiac-motor interconnection and frailty. Adding CCM parameters in a multimodal model may provide a promising measure of frailty.
Polyborosiloxane（PBS） is a supramolecular material， the material itself has a physical cross-linked network structure, and has the characteristics of high and low temperature resistance, weather resistance, electrical insulation and self-healing. However, the mechanical properties of polyborosiloxane material prepared by existing methods are poor, which limits its application in some fields. In order to improve the mechanical properties of polyborosiloxane and broaden the application field of the material, in this paper, a vinyl-containing polyborosiloxane was prepared by reacting high-molecular-weight polymethylvinylsiloxane（VMQ）with boric acid（BA）at high temperature. The polyborosiloxane composite material with surface self-adhesiveness was obtained by introducing vinyl structure and fumed white carbon black and undergoing thermal vulcanization treatment. The structure, dynamic mechanical properties, thermal stability, mechanical properties and self-adhesive properties of polyborosiloxane composite were measured, and the formation of B—O—Si structure was confirmed by infrared reflection spectroscopy. The results show that the B∶O dynamic bond is formed inside the polyborosiloxane composite material, and the surface of the material has certain self-adhesive properties. The peel strength formed by self-adhesion can reach 4 N/cm, the tensile strength is 4.154 MPa, and 5 % thermal mass loss temperature is 394.8 °C, which means that the sample has good mechanical properties and thermal stability.
Abstract Non-stationary characteristic in nature wind has a great effect on buffeting performance of long-span bridges. The influence of key parameters in non-stationary wind velocity models on nonlinear buffeting responses of a super long-span suspension bridge was investigated in this paper. Firstly, four non-stationary wind velocity models are established by combing the time-varying average wind velocity with an exponential function and the fluctuating wind velocity with four modulation functions, respectively. These non-stationary wind velocity models have obvious non-stationary characteristics and then are validated by the classical power spectrum densities. Finally, three displacement responses of the bridge deck under four different independent variables of β in the exponential function and four modulation functions were compared, respectively. Results show that the turbulence intensities using two non-uniform modulation functions (NMF) are larger than those using uniform modulation functions (uMF). Moreover, the root mean square (RMS) values of three displacement responses increase with the decrease of β. Besides, the RMS values of three displacement under two NMFs are larger than those under two uMFs, and their RMS values under the second uMF are the smallest.
Engineering (General). Civil engineering (General), Motor vehicles. Aeronautics. Astronautics