Motion Trajectory Research Articles

Effects of Medium and Flow Rate on the Film-Forming Structures of B10 Cu-Ni Alloys and Their Resistance to Corrosion Caused by Sulfate-Reducing Bacteria

The effects of medium and flow rate on the film-forming structures of B10 Cu-Ni alloys and their resistance to corrosion caused by sulfate-reducing bacteria are investigated in this article. Combined with a predicted cloud map of pipeline corrosion area and a particle motion trajectory map obtained using Computational Fluid Dynamics (CFD), the growth law of alloy passivation films was analyzed and the pitting process of sulfate-reducing bacteria (SRB) on passivation films was revealed. The results show that the film formation effect is best when the stream of water in the film-forming environment is filtered seawater with a flow rate of 0.8 m/s, which consists of a uniform and dense gray-brown passivated film layer with the strongest resistance to SRB corrosion. When the flow rate is 0 m/s, the clay particles in the seawater cover the surface of the passivation film, hindering the contact of oxygen with the substrate and inhibiting the growth of the passivation film. When the stream of water in the film-forming environment is seawater with a flow rate of 3 m/s, the surface of the substrate shows obvious scouring marks, which is favorable for the enrichment of SRB and further accelerates the pitting corrosion of the substrate. Cl− has a significant influence on the formation of passivation films on B10 Cu-Ni alloys. When the filming medium is deionized water, the B10 Cu-Ni alloy does not form a complete passivation film at all flow rates.

Remote sensing reveals the role of forage quality and quantity for summer habitat use in red deer

BackgroundThe habitat use of wild ungulates is determined by forage availability, but also the avoidance of predation and human disturbance. They should apply foraging strategies that provide the most energy at the lowest cost. However, due to data limitations at the scale of movement trajectories, it is not clear to what extent even well-studied species such as red deer (Cervus elaphus) trade-off between forage quality and quantity, especially in heterogeneous alpine habitats characterized by short vegetation periods.MethodsWe used remote sensing data to derive spatially continuous forage quality and quantity information. To predict relative nitrogen (i.e. forage quality) and biomass (i.e. forage quantity), we related field data to predictor variables derived from Sentinel-2 satellite data. In particular, our approach employed random forest regression algorithms, integrating various remote sensing variables such as reflectance values, vegetation indices and optical traits derived from a radiative transfer model. We combined these forage characteristics with variables representing human activity, and applied integrated step selection functions to estimate sex-specific summer habitat selection of red deer in open habitats within and around the Swiss National Park, an alpine Strict Nature Reserve.ResultsThe combination of vegetation indices and optical traits greatly improved predictive power in both the biomass (R2 = 0.60, Root mean square error (RMSE) = 88.55 g/m2) and relative nitrogen models (R2 = 0.34, RMSE = 0.28%). Both female and male red deer selected more strongly for biomass (estimate = 0.672 ± 0.059 SE for normalised values for females, and 0.507 ± 0.061 for males) than relative nitrogen (estimate = 0.124 ± 0.062 for females, and 0.161 ± 0.061 for males, respectively). Females showed higher levels of use of the Swiss National Park.ConclusionsRed deer in summer habitats select forage quantity over quality with little difference between sexes. Females respond more strongly to human activities and thus prefer the Swiss National Park. Our results demonstrate the capability of satellite data to estimate forage quality and quantity separately for movement ecology studies, going beyond the exclusive use of conventional vegetation indices.

Phase-amplitude reduction-based imitation learning

In this study, we propose the use of the phase-amplitude reduction method to construct an imitation learning framework. Imitating human movement trajectories is recognized as a promising strategy for generating a range of human-like robot movements. Unlike previous dynamical system-based imitation learning approaches, our proposed method allows the robot not only to imitate a limit cycle trajectory but also to replicate the transient movement from the initial or disturbed state to the limit cycle. Consequently, our method offers a safer imitation learning approach that avoids generating unpredictable motions immediately after disturbances or from a specified initial state. We first validated our proposed method by reconstructing a simple limit-cycle attractor. We then compared the proposed approach with a conventional method on a lemniscate trajectory tracking task with a simulated robot arm. Our findings confirm that our proposed method can more accurately generate transient movements to converge on a target periodic attractor compared to the previous standard approach. Subsequently, we applied our method to a real robot arm to imitate periodic human movements.

AI‐Driven Multi‐Modal Information Fusion System for Real Applications in Physical Training

ABSTRACTThis paper introduces an AI‐driven multi‐modal information fusion system tailored for real‐world physical training applications, integrating state‐of‐the‐art computer vision, deep learning, and multi‐sensor technologies. The system combines high‐precision cameras and inertial measurement unit (IMU) sensors to capture real‐time motion trajectories and posture data. These data are processed through convolutional neural networks (CNNs) and long short‐term memory (LSTM) models for accurate movement analysis. Additionally, augmented reality (AR) provides real‐time visual feedback, while deep reinforcement learning algorithms deliver personalized training recommendations. In an 8‐week study involving 100 high school students, the system improved movement accuracy by 23% and received a 4.6/5 user satisfaction rating. Although challenges remain in optimizing computational efficiency and user interface design, the system's modular and scalable nature makes it an adaptable solution for enhancing physical training across educational, home, and professional settings.

Quantifying Similarities Between MediaPipe and a Known Standard to Address Issues in Tracking 2D Upper Limb Trajectories: Proof of Concept Study.

Markerless motion tracking methods have promise for use in a range of domains, including clinical settings where traditional marker-based systems for human pose estimation are not feasible. Artificial intelligence (AI)-based systems can offer a markerless, lightweight approach to motion capture. However, the accuracy of such systems, such as MediaPipe, for tracking fine upper limb movements involving the hand has not been explored. The aim of this study is to evaluate the 2D accuracy of MediaPipe against a known standard. Participants (N=10) performed a touchscreen-based shape-tracing task requiring them to trace the trajectory of a moving cursor using their index finger. Cursor trajectories created a reoccurring or random shape at 5 different speeds (500-2500 ms, in increments of 500 ms). Movement trajectories on each trial were simultaneously captured by the touchscreen and a separate video camera. Movement coordinates for each trial were extracted from the touchscreen and compared to those predicted by MediaPipe. Specifically, following resampling, normalization, and Procrustes transformations, root-mean-squared error (RMSE; primary outcome measure) was calculated between predicted coordinates and those generated by the touchscreen computer. Although there was some size distortion in the frame-by-frame estimates predicted by MediaPipe, shapes were similar between the 2 methods and transformations improved the general overlap and similarity of the shapes. The resultant mean RMSE between predicted coordinates and those generated by the touchscreen was 0.28 (SD 0.06) normalized px. Equivalence testing revealed that accuracy differed between MediaPipe and the touchscreen, but that the true difference was between 0 and 0.30 normalized px (t114=-3.02; P=.002). Additional analyses revealed no differences in resultant RMSE between methods when comparing across lower frame rates (30 and 60 frames per second [FPS]), although there was greater RMSE for 120 FPS than for 60 FPS (t35.43=-2.51; P=.03). Overall, we quantified similarities between one AI-based approach to motion capture and a known standard for tracking fine upper limb movements, informing applications of such systems in domains such as clinical and research settings. Future work should address accuracy in 3 dimensions to further validate the use of AI-based systems, including MediaPipe, in such domains.

Functional, instrumental test of flexion-extension movements of the wrist joint. Normative parameters.

Background: Stroke is a significant medical and social issue due to its high incidence and mortality rates, with a trend toward increasing overall cases. In 80% of patients, upper limb dysfunction persists. Existing methods, such as clinical scales and questionnaires, are criticized for their subjectivity and lack of precision. There is a need to develop an instrumental method for assessing upper limb function that can be applied in clinical settings. Aims: To develop a functional test for the objective diagnosis of wrist joint function applicable in clinical settings. Methods: A functional test was proposed to assess the biomechanics of the wrist joint using inertial sensors. The study involved 15 healthy volunteers (5 men and 10 women, aged 23 to 33), with no joint diseases or neurological disorders. The study was conducted over one year (2022–2023). The primary endpoint was to determine the amplitude, time, and trajectory of wrist joint movements during two tests—"Wrist-0" and "Wrist-Flex." Evaluation was based on the duration of the movement cycle, maximum amplitude, and movement phase. Results: Assessment of upper limb function using clinical scales (ARAT, FMA-UE, MRC) showed that parameters were consistent with those of healthy individuals. In the "Wrist-0" test, movement amplitude was significantly lower compared to the "Wrist-Flex" test (p0.05). There were no statistically significant differences in movement amplitude between the right and left limbs in either test (p0.05). The phase of maximum flexion in the "Wrist-0" test occurred significantly earlier than in the "Wrist-Flex" test for the right hand (p0.05). The movement cycle duration did not differ significantly between tests for the right hand (p0.05) but was significantly longer in the "Wrist-Flex" test for the left hand (p0.05). Conclusions: Normative parameters for the functional test have been established. Minor differences in function between the right and left wrist joints were observed. The proposed test requires minimal time to administer and can be used for the objective diagnosis of wrist joint function in patients.

Roll angle measurement method based on a double-sided optical wedge

The roll angle is an important albeit difficult-to-measure geometric parameter. This study proposes a roll angle measurement method for an entire circle (360°). An elaborately designed double-sided optical wedge (DOW) is employed as a roll angle probe to divide the incident light beam into two beams. Both beams are imaged using a photodetector, and the roll angle of the DOW is obtained through the detection of the motion trajectories of the two images. The measurement principle is comprehensively analyzed, and the experimental results indicate that the accuracy of the constructed measurement system is better than ±7.72arcsec in the range of 360°. The proposed method is confirmed to be effective for large-range roll angle measurements.

Decoding continuous motion trajectories of upper limb from EEG signals based on feature selection and nonlinear methods.

Brain-computer interface (BCI) system has emerged as a promising technology that provides direct communication and control between the human brain and external devices. Among the various applications of BCI, limb motion decoding has gained significant attention due to its potential for patients with motor impairment to regain independence and improve their quality of life. However, the reconstruction of continuous motion trajectories in BCI systems based on electroencephalography (EEG) signals remains a challenge in practical life. This study investigates the feasibility of applying feature selection and nonlinear regression for decoding motion trajectory from EEG. We propose to fix the time window, select the optimal feature set, and reconstruct the motion trajectory of motor execution tasks using polynomial regression. The proposed approach is validated on a public dataset consisting of EEG and hand position data recorded from 15 subjects. Several methods including ridge regression and multiple linear regression are employed as comparisons. The cross-validation results show that the proposed reconstructed method has the highest correlation with actual motion trajectories, with an average value of 0.511±0.019 (p<0.05). This finding demonstrates the great potential of our approach for real-world motor kinematics BCI applications.

Plasmon-Enhanced Optoelectronic Graded Neurons for Dual-Waveband Image Fusion and Motion Perception.

Motion recognition based on vision detectors requires the synchronous encoding and processing of temporal and spatial information in wide wavebands. Here, the dual-waveband sensitive optoelectronic synapses performing as graded neurons are reported for high-accuracy motion recognition and perception. Wedge-shaped nanostructures are designed and fabricated on molybdenum disulfide (MoS2) monolayers, leading to plasmon-enhanced wideband absorption across the visible to near-infrared spectral range. Due to the charge trapping and release at shallow trapping centers within the device channel, the optoelectronic graded neurons demonstrate remarkable photo-induced conductance plasticity at both 633 and 980nm wavelengths. A dynamic vision system consisting of 20 × 20 optoelectronic neurons demonstrates remarkable capabilities in the precise detection and perception of various motions. Moreover, neural network computing systems have been built as visual motion perceptron to identify target object movement. The recognition accuracy of dual-wavelength fused images for various motion trajectories has experienced a remarkable enhancement, transcending the previous level of less than 80% to impressive values exceeding 99%.

Exploring How Aerosol Optical Depth Varies in the Yellow River Basin and Its Urban Agglomerations by Decade

In this study, the spatial–temporal characteristics of AOD in the Yellow River Basin (YRB) and urban agglomerations within the basin were analyzed at a 1 km scale from 2011 to 2020 based on the MCD19A2 AOD dataset. This study shows the following: (1) From 2011 to 2020, the AOD value of the YRB showed a declining trend, with 96.011% of the zones experiencing a decrease in AOD. The spatial distribution of AOD displayed a pattern of high in the east, low in the west, high in the south, and low in the north. The rate of decline showed a distribution pattern of fast in the southeast and slow in the northwest. (2) The AOD in the YRB showed similar characteristics in different seasons: the south and east were consistently higher than the north and west. The seasonal AOD values in the YRB showed the following pattern: summer &gt; spring &gt; autumn &gt; winter. The AOD values of urban agglomeration were basically larger in spring and summer. (3) The SDE and mean center of the yearly AOD were located in the southeast and Shanxi Province, with the movement from southeast to northwest. It can be divided into three stages based on the movement trajectory: northeast–southwest round-trip movement (2011–2014), one-way movement to the northwest (2014–2018), and southeast–northwest round-trip movement (2018–2020).

An Underactuated Dexterous Hand with Novel Bidirectional Self-Locking Joints for Multiple Fingertip Active Motion Trajectories

This paper proposes an underactuated dexterous hand with novel bidirectional self-locking joints (BSJs) that enable multiple fingertip motion trajectories. The BSJ design integrates a locking wheel, rack, finger side walls, and a self-holding electromagnetic actuator, combining rack-and-pinion transmission with friction self-locking principles. Building on the BSJ concept, an underactuated dexterous hand is developed. The study begins with an analysis of BSJ’s deviation angle, establishing the minimum deviation angle critical to its operation. A detailed mechanical model of a BSJ is formulated, and its parameters are quantitatively analyzed to determine a safety static friction coefficient (0.177). Five distinct finger motion modes are designed and kinematic analysis focuses on the index finger and the generation of 57 unique fingertip active motion trajectories. Experimental validation included single finger performance tests that confirmed the diversity of fingertip trajectories and the hand’s ability to withstand loading in both forward and reverse directions. Through envelope and precision grasping experiments, the dexterous hand demonstrated its adaptability and ability to grasp objects of various sizes and shapes, such as strawberries, apples, student ID cards, and water bottles. This capability underscores its potential for a wide range of applications, from prosthetic hands for rehabilitation, where precision and adaptability are key, to robotic hands in industrial automation, offering flexibility in diverse tasks.

Monte Carlo tree search with spectral expansion for planning with dynamical systems.

The ability of a robot to plan complex behaviors with real-time computation, rather than adhering to predesigned or offline-learned routines, alleviates the need for specialized algorithms or training for each problem instance. Monte Carlo tree search is a powerful planning algorithm that strategically explores simulated future possibilities, but it requires a discrete problem representation that is irreconcilable with the continuous dynamics of the physical world. We present Spectral Expansion Tree Search (SETS), a real-time, tree-based planner that uses the spectrum of the locally linearized system to construct a low-complexity and approximately equivalent discrete representation of the continuous world. We prove that SETS converges to a bound of the globally optimal solution for continuous, deterministic, and differentiable Markov decision processes, a broad class of problems that includes underactuated nonlinear dynamics, nonconvex reward functions, and unstructured environments. We experimentally validated SETS on drone, spacecraft, and ground vehicle robots and one numerical experiment, each of which is not directly solvable with existing methods. We successfully show that SETS automatically discovers a diverse set of optimal behaviors and motion trajectories in real time.

Prospective motion correction for and susceptibility mapping using spherical navigators.

To perform prospective motion correction (PMC) for improved and susceptibility mapping using a purely navigator-based approach. Spherical navigators (SNAVs) were combined with an additional FID readout for simultaneous measurement of motion and zeroth-order field shifts. The resulting FIDSNAVs were interleaved for PMC of a multi-echo gradient echo sequence with retrospective correction. Experiments were performed on a 3T scanner with a 32-channel head coil. Performance was assessed in five volunteers with motion prompts derived from real unintentional motion trajectories. At short TEs, PMC alone was sufficient to achieve good image quality; at longer TEs, retrospective correction was often just as important for artifact reduction as motion correction. Both PMC and retrospective correction reduced error in and susceptibility maps for all participants. Residual artifacts were observed in the most severe motion case. Combining SNAVs with an additional FID readout enables simultaneous motion and field correction with no additional hardware requirements, improving the fidelity of quantitative mapping in the presence of motion.

Atheist Religion and Worldly Redemption in China: The 1922 Anti-Christian Movement

Abstract: The Anti-Christian Movement started out as a nationalist and cultural movement. Although scientific rationality invalidated theistic religion for many New Culturalist intellectuals, they shared the aspiration for the creation of an atheist religion that related personal moral rectitude to the worldly redemption of national salvation. Following the intervention of the Comintern and the Chinese Communist Party, the Movement took a political turn with a communist overtone. The political atheist religion of communism progressively overshadowed the atheist religion that stressed individual moral improvement. The latter was considered to be too impotent to bring about sociopolitical changes in comparison to political organization and mobilization that communism promised to concretize. As such, the shift of the intellectual and political paradigms in which the Anti-Christian Movement developed in a time scope of five years presented a panorama of the trajectory of the New Culture Movement, and its political legacy continued to inform China’s propaganda of faith after 1949.

Numerical Simulation of the Distribution Patterns of Particle Deposition on the Vertical Wall Behind Near-Wall Heat Source

Near-wall heat sources have a crucial part to play in the process of particle deposition. Thus, this study investigates the impact of the near-wall heat source on the distribution patterns of particle deposition on the vertical wall behind the heat source, taking into account the variability in heat source temperatures and distances from the vertical wall. A model based on the Eulerian–Lagrangian method was established for tracking the motion trajectories of 1000 particles with a density of 1400 kg/m3 and a particle size range of 0.01–10.0 μm. The temperature field, airflow field, and particle deposition distribution in six cases were analyzed. It was shown that the heat source temperature significantly affectis the temperature field, airflow field, and particle deposition distribution on the vertical wall behind the heat source. This study demonstrated that as the temperature rises, the quantity of particles deposited in the upper-right region of the vertical wall decreases more noticeably. The quantity of particles deposited onto the vertical wall is inversely related to the distance between the near-wall heat source and the vertical wall. On one hand, the deposition distribution law serves as a foundation for advancing the technology aimed at removing suspended particles via thermal plumes. On the other hand, it provides critical insights for addressing the challenges associated with harmful particle deposition linked to the attachment effects of thermal plumes.

Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss

Precise trajectory planning is crucial in terms of enabling unmanned aerial vehicles (UAVs) to execute interference avoidance and target capture actions in unknown environments and when facing intermittent target loss. To address the trajectory planning problem of UAVs in such conditions, this paper proposes a UAV trajectory planning system that includes a predictor and a planner. Specifically, the system employs a bidirectional Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) network algorithm with an adaptive attention mechanism (BITCN-BIGRU-AAM) to train a model that incorporates the historical motion trajectory features of the target and motion intention the inferred by a Dynamic Bayesian Network (DBN). The resulting predictions of the maneuvering target are used as terminal inputs for the planner. An improved Radial Basis Function (RBF) network is utilized in combination with an offline–online trajectory planning method for real-time obstacle avoidance trajectory planning. Additionally, considering future practical applications, the predictor and planner adopt a parallel optimization and correction algorithm structure to ensure planning accuracy and computational efficiency. Simulation results indicate that the proposed method can accurately avoid dynamic interference and precisely capture the target during tasks involving dynamic interference in unknown environments and when facing intermittent target loss, while also meeting system computational capacity requirements.

Thermodynamic and kinetic investigation on crystallization of photosensitive glass-ceramics via molecular dynamics

Photosensitive glass-ceramics serves as a crucial support for advancement of integrated circuits in post Moore era. Nevertheless, current research lacks an in-depth discussion of crystallization and nucleation mechanisms induced by nucleating agents. Here, we select new AgSbO3 nucleating agents and conduct a detailed thermodynamic and kinetic investigation of its promoting effects in Li2SiO3 using first-principles molecular dynamics. Statistical results from pair distribution functions indicate that in the presence of AgSbO3 nucleating agents in Li2SiO3, the structure tends to exhibit a more crystalline distribution. The diffusion coefficient of Li ions in AgSbO3–Li2SiO3 is 0.47 × 10–6 cm2/s, which is one-tenth of the value of Li ions in amorphous Li2SiO3. Additionally, a molecular dynamics approach was employed to screen and analyze motion trajectories of 16 lithium ions. All of these data suggest that AgSbO3 nucleating agents can accelerate crystallization of Li2SiO3. This work provides an atomic-scale discussion of nucleating agents' effects in photosensitive glass-ceramics.

Unmanned aerial vehicle formation control method based on improved artificial potential field and consensus

AbstractFormation coordination can improve the overall performance of unmanned aerial vehicle (UAV) systems, so it is important to realize coordinated formation control. To solve the problems of ineffective internal collision avoidance and communication distance maintenance in existing formation control methods, a UAV formation control algorithm based on an improved artificial potential field and consensus is proposed. First, an improved artificial potential field obstacle avoidance method is used to solve the problems of collision avoidance and communication distance maintenance within the formation by setting a communication risk zone and a collision risk zone and by defining the dynamic adjustment parameters of the inter‐UAV virtual force to improve the traditional artificial potential field method. Second, a time integrating factor is introduced, and an improved consensus‐based formation control method is employed to overcome the problem of the artificial potential field method falling into the local optimum easily. Simulation experiments were designed to analyse the trends of the UAV formation motion trajectory, inter‐UAV relative distance, attitude, and formation time. The analysis results show that the method can make the UAV formation consistent with the flight trajectory of the intended formation, solving the problems of collision avoidance and communication distance maintenance within the UAV formation.

Ascertainment of community exposure sites to Ross River virus during the 2020 outbreak in Brisbane, Australia.

This study investigated potential Ross River virus (RRV) exposure sites in Greater Brisbane during the Queensland COVID-19 lockdown (January-July 2020). Using RRV notifications, cluster identification techniques, and mobile phone data for movement network analysis, the study examined 993 RRV cases and 9 million movement trajectories from residential RRV cluster areas (hot-spots). The findings revealed that population movement was a key risk factor to RRV incidence within hotspots whereby highly interconnected areas had more RRV cases during lockdown. While environmental conditions within RRV hot-spot were less significant compared to their connectivity, areas with higher vegetation density had fewer RRV cases. The study also noted that individuals from RRV hot-spots spent less time in green areas pre-lockdown than during and after lockdown. The results suggest that population movement significantly influenced the 2020 RRV outbreak. These insights can help adapt current vector control and surveillance protocols to target areas identified in this study.

PHYSICO-MECHANICAL PROPERTIES OF METALLIC PARTS ELECTROCHEMICALLY PRINTED USING COPPER NITRATE ELECTROLYTE

The process of electrochemical 3D printing of copper parts using a concentrated copper nitrate electrolyte has been investigated. It was established that in a nitrate copper plating electrolyte with a copper nitrate concentration of 500 g/l, it is possible to obtain locally electrodeposited metal parts with a compact fine-crystalline structure and an average profile height of 150 μm. The working current densities of electrochemical printing in this case were 2.45…2.7 A/dm2. It has been shown that reducing the speed of movement of the working electrode (anode) leads to an improvement in the uniformity of metal deposition along the entire trajectory of movement and the reduction of the crystals size of the metal deposit. This is obviously a consequence of a change in the current mode of electrodeposition. It was established that on the surface with initial parameters of microroughness (Rz(1)=1.128 Ra(1)=0.2925) during electrochemical 3D printing, a metal structure with roughness parameters – Rz(1)=95.72 Ra(1)=16.32 has been formed. The formation of a compact but at the same time a highly rough metal structure makes the application of the electrochemical 3D printing method promising in the production technology of printed circuit boards and in the field of microelectronics as a whole. The micromechanical tests of samples of electrochemically printed copper deposits showed the following. The microhardness of the electrochemically printed copper deposit is approximately 30% higher, but the plasticity coefficient and Young’s modulus acquire values close to the corresponding parameters of hydrometallurgical copper. This indicates that the method of obtaining metal objects by electrochemical 3D printing does not contribute to a significant deterioration of the elasticity of the obtained material.