Search Results (1,952)

An acoustic Doppler current profiler (ADCP) installed on a platform produces rotational tangential velocity as a result of variations in the platform’s attitude, with both the tangential velocity and radial orientation varying between each pulse’s transmission and reception by the transducer. These factors introduce errors into the measurements of vessel velocity and flow velocity. In this study, we address the errors induced by dynamic factors related to variations in attitude and propose an ADCP attitude dynamic error correction method based on angular velocity tensor and radius vector estimation. This method utilizes a low-sampling-rate inclinometer and compass data and estimates the angular velocity tensor based on a physical model of vessel motion combined with nonlinear least-squares estimation. The angular velocity tensor is then used to estimate the transducers’ radius vectors. Finally, the radius vectors are employed to correct the instantaneous tangential velocity within the measured velocities of the vessel and flow. To verify the effectiveness of the proposed method, field tests were conducted in a water pool. The results demonstrate that the proposed method surpasses the attitude static correction approach. In comparison with the ASC method, the average relative error in vessel velocity during free-swaying movement decreased by 20.94%, while the relative standard deviation of the error was reduced by 17.38%. Full article

The resolution of a mega-electron-volt scanning transmission electron microscope (MeV-STEM) is primarily governed by the properties of the incident electron beam and angular broadening effects that occur within thick biological samples and microchips. A precise understanding and mitigation of these constraints require detailed knowledge of beam emittance, aberrations in the STEM column optics, and energy-dependent elastic and inelastic critical angles of the materials being examined. This simulation study proposes a standardized experimental framework for comprehensively assessing beam intensity, divergence, and size at the sample exit. This framework aims to characterize electron-sample interactions, reconcile discrepancies among analytical models, and validate Monte Carlo (MC) simulations for enhanced predictive accuracy. Our numerical findings demonstrate that precise measurements of these parameters, especially angular broadening, are not only feasible but also essential for optimizing imaging resolution in thick biological samples and microchips. By utilizing an electron source with minimal emittance and tailored beam characteristics, along with amorphous ice and silicon samples as biological proxies and microchip materials, this research seeks to optimize electron beam energy by focusing on parameters to improve the resolution in MeV-STEM/TEM. This optimization is particularly crucial for in situ imaging of thick biological samples and for examining microchip defects with nanometer resolutions. Our ultimate goal is to develop a comprehensive mapping of the minimum electron energy required to achieve a nanoscale resolution, taking into account variations in sample thickness, composition, and imaging mode. Full article

Ψ-1,4-naphthoquinones (Ψ-NQ) are non-quinoid compounds in which aromaticity—found in 1,4-naphthoquinones—is broken by the introduction of an angular methyl at C-4a or -8a. This series was designed to act as prodrugs of 1,4-naphthoquinones in an oxidative environment. Furthermore, from a medicinal chemistry point of view, the loss of planarity of the scaffold might lead to an improved solubility and circumvent the bad reputation of quinones in the pharmaceutical industry. In this work, we illustrated the concept by the synthesis of Ψ -plasmodione regioisomers as prodrugs of the antimalarial plasmodione. The presence of a chiral center introduces a new degree of freedom to be controlled by enantioselectivity and regioselectivity of the cycloaddition in the Diels–Alder reaction. The first strategy that was followed was based on the use of a chiral enantiopure sulfoxide to govern the stereoselective formation of (+)Ψ-NQ or (−)Ψ-NQ, depending on the chirality of the sulfoxide (R or S). New sulfinylquinones were synthesized but were found to be ineffective in undergoing cycloaddition with different dienes under a wide range of conditions (thermal, Lewis acid). The second strategy was based on the use of boronic acid-substituted benzoquinones as auxiliaries to control the regioselectivity. Using this methodology to prepare the (±)Ψ-NQ racemates, promising results (very fast cycloaddition time: ~2 h) were obtained with boronic acid-based quinones 25 and 27 in the presence of 1-methoxy-1,3-butadiene, to generate the 4a- and the 8a-Ψ-plasmodione regioisomers 1 and 2 (synthesized in six steps with a total yield of 10.5% and 4.1%, respectively. As the expected prodrug effect can only be revealed if the molecule undergoes an oxidation of the angular methyl, e.g., in blood-feeding parasites that digest hemoglobin from the host, the antimalarial and the antischistosomal properties of both (±)Ψ-NQ regioisomers were determined in drug assays with Plasmodium falciparum and Schistosoma mansoni. Metabolic studies under quasi-physiological conditions and LC-MS analyses were undertaken to reveal the generation of plasmodione from both the 4a- and the 8a-Ψ-plasmodione regioisomers. Full article

Earlier, a significant enhancement of the nondipole parameters γ2p, δ2p, and ζ2p=γ2p+3δ2p in the photoelectron angular distribution for Ne 2p photoionization was predicted, owing to resonance interference between dipole (E1) and quadrupole (E2) transitions. This enhancement manifests as narrow resonance spikes in the parameters due to the low-energy 2s→3p and 2s→4p dipole, as well as the 2s→3d quadrupole autoionizing resonances. Given the unique nature of this predicted enhancement, it requires further validation, specifically regarding whether these narrow spikes in γ2p, δ2p and ζ2p will or will not retain their values for experimental observation if one accounts for a typical finite frequency spread in the ionizing radiation. To address this, we revisit the previous study, now incorporating the effect of frequency spread in the ionizing radiation, assuming a spread as large as 5 meV at the half-maximum of the radiation’s intensity. In the present paper we demonstrate that while the frequency spread does affect the resonance enhancement of γ2p, δ2p and ζ2p, these parameters still retain quantitatively significant values to be observed experimentally. The corresponding calculations were performed using the random phase approximation with exchange, which accounts for interchannel coupling in both dipole and quadrupole photoionization amplitudes. Full article

This paper proposes an Adaptive Robust Control (ARC) strategy for pump-controlled pitch systems in large wind turbines to address challenges in control accuracy and energy efficiency. First, a mathematical model integrating pitch angle dynamics and hydraulic characteristics is established, with pitch angle, pitch angular velocity, and hydraulic cylinder thrust as state variables. Then, an ARC strategy is designed using the backstepping method and incorporating parameter adaptation to handle system nonlinearities and uncertainties. The controller parameters are optimized using Particle Swarm Optimization (PSO) under wind disturbance conditions, and comparative analyses are conducted with traditional PID control. The numerical simulation results show that both controllers achieve similar tracking performance under nominal conditions, with PID achieving a 0.08° maximum error and ARC showing a 0.1° maximum error. However, the ARC strategy demonstrates superior robustness under parameter variations, maintaining tracking errors below 0.15°, while the PID error increases to 1.5°. Physical test bench experiments further validate these findings, with ARC showing significantly better performance during cylinder retraction with 0.1° error compared to PID’s 0.7° error. The proposed control strategy effectively handles both the inherent nonlinearities of the pump-controlled system and external disturbances, providing a practical solution for precise pitch control in large wind turbines while maintaining energy efficiency through the pump-controlled approach. Full article

Symmetry affects aesthetic judgements, and it has been extensively studied at least for faces and abstract objects. We examined the role of bilateral symmetry on aesthetic judgements across different types of stimuli. It is important to test if symmetry is equally effective in the context of objects expected to be symmetrical, e.g., faces, and objects that are almost never symmetrical, e.g., landscapes. We used the following categories: angular shapes, smooth shapes, landscapes, flowers, female faces, and male faces. We selected these image categories considering their differing degrees of curvature, familiarity to the viewer, and tendency to be perceived as ensembles, as opposed to standalone objects. We also included blobs, manipulating landscapes into blurry tessellated patterns featuring patches of color, to remove familiarity while preserving the appearance of ensembles. For each item in these categories, images were modified to obtain perfect bilateral symmetry. We collected both explicit ratings and implicit measures of symmetry preference. For landscapes, there was a clear preference for the non-symmetrical (original) version. We observed a dissociation between explicit and implicit measures. Implicit measures demonstrated positive associations for all categories. Full article

Motor skill development is crucial in human growth, evolving with the maturation of the nervous and musculoskeletal systems. Quantifying these skills, especially coordinative abilities, remains challenging. This study aimed to assess the performance of five motor tasks in children and adolescents using high-speed video analysis, providing data for movement and health professionals. Seventy-two volunteers were divided into three age groups: 27 first-grade primary school students (19 males and 8 females, aged 6.5 ± 0.5 years), 35 fourth-grade primary school students (16 males and 19 females, aged 9.2 ± 0.4 years), and 28 s-year middle school students (16 males and 12 females, aged 13.0 ± 0.3 years). Participants performed five motor tasks: standing long jump, running long jump, stationary ball throw, running ball throw, and sprint running. Each task was recorded at 120 frames per second and analyzed using specialized software to measure linear and angular kinematic parameters. Quantitative measurements were taken in the sagittal plane, while qualitative observations were made using a dichotomous approach. Statistical analysis was performed using the Kruskal–Wallis and Mann–Whitney tests with Bonferroni correction. Significant differences were observed across age groups in various parameters. In the standing long jump, older participants exhibited a longer time between initial movement and maximum loading. The running long jump revealed differences in the take-off angle, with fourth-grade students performing the best. Ball-throwing tests indicated improvements in the release angle with age, particularly in females. Sprint running demonstrated the expected improvements in time and stride length with age. Gender differences were notable in fourth-grade students during the running long jump, with females showing greater knee flexion, while males achieved better take-off angles. Video analysis effectively identified age-related and gender-specific differences in motor skill performance. The main differences were measured between first-grade primary school and second-year middle school students while gender differences were limited to all age groups. This method provides valuable insights into motor development trajectories and can be used by professionals to objectively assess and monitor the technical aspects of motor skills across different age groups. Full article

Balance control, a complex sensorimotor skill, declines with age. Assessing balance is crucial for identifying fall risk and implementing interventions in the older population. This study aimed to measure age-dependent changes in static and dynamic balance using inertial measurement units in a clinical setting. This study included 82 healthy participants aged 20–85 years. For the dynamic balance test, participants stood on a horizontally swaying balance board. For the static balance test, they stood on one leg. Inertial measurement units attached to their bodies recorded kinematic data, with average absolute angular velocities assessing balance capabilities. In the dynamic test, the younger participants had smaller average absolute angular velocities in most body parts than those of the middle-aged and older groups, with no significant differences between the middle-aged and older groups. Conversely, in the single-leg stance tests, the young and middle-aged groups outperformed the older group, with no significant differences between the young and middle-aged groups. Thus, dynamic and static balance decline at different stages with age. These results highlight the complementary role of inertial measurement unit-based evaluation in understanding the effect of age on postural control mechanisms, offering valuable insights for tailoring rehabilitation protocols in clinical settings. Full article

Unmanned aerial vehicles (UAVs) face significant challenges in maintaining stability when subjected to external wind disturbances and internal noise. This paper addresses these issues by introducing a real-time wind speed fitting algorithm and a wind field model that accounts for varying wind conditions, such as wind shear and turbulence. To improve control in such conditions, a deep reinforcement learning (DRL) strategy is developed and tested through both simulations and real-world experiments. The results indicate a 65% reduction in trajectory tracking error with the DRL controller. Additionally, a UAV built for testing exhibited enhanced stability and reduced angular deviations in wind conditions up to level 5. These findings demonstrate the effectiveness of the proposed DRL-based control strategy in increasing UAV resilience to wind disturbances. Full article

The conditions in Mira variable atmospheres make them wonderful laboratories to study a variety of stellar physics such as molecule–grain formation, dust production, shock chemistry, stellar winds, mass loss, opacity-driven pulsation, and shocks. We were awarded an NSF grant to analyze over a decade of synoptic observations from the Palomar Testbed Interferometer (PTI) of 106 Miras to curate a Mira Reference Dataset. The Miras included in this dataset include M-types, S-types, and C-types, and span a wide range of pulsation periods. PTI measured K-band angular sizes that when combined with a distance allow us to directly determine fundamental stellar parameters such as effective temperature, radial size, and bolometric flux. Supplementing observations with interferometric measurements of the stars opens the Mira laboratory to a wealth of different experiments. We provide two case studies to serve as examples of the power of the Mira Reference Dataset. The first case study describes combining PTI measurements with Spitzer IRS spectra of M-type Miras, which allowed us to fully characterize CO2 gas in their atmospheres. The second case study examines how PTI narrow-band data can be used to study phase-dependent pulsation effects on the stellar atmosphere. We provide a list of all the Miras (with coordinates) included in the set for anyone who would like to add them to their observing programs. All the data we produce and collate for this Mira Reference Dataset will be hosted and curated on a website open to the public so that other researchers and citizen scientists can participate in expanding the utility and body of knowledge on this set of “wonderful” stars. Full article

Automatic Speech Emotion Recognition (SER) plays a vital role in making human–computer interactions more natural and effective. A significant challenge in SER development is the limited availability of diverse emotional speech datasets, which hinders the application of advanced deep learning models. Transfer learning is a machine learning technique that helps address this issue by utilizing knowledge from pre-trained models to improve performance on a new task in a target domain, even with limited data. This study investigates the use of transfer learning from various pre-trained networks, including speaker embedding models such as d-vector, x-vector, and r-vector, and image classification models like AlexNet, GoogLeNet, SqueezeNet, ResNet-18, and ResNet-50. We also propose enhanced versions of the x-vector and r-vector models incorporating Multi-Head Attention Pooling and Angular Margin Softmax, alongside other architectural improvements. Additionally, reverberation from the Room Impulse Response datasets was added to the speech utterances to diversify and augment the available data. Notably, the enhanced r-vector model achieved classification accuracies of 74.05% Unweighted Accuracy (UA) and 73.68% Weighted Accuracy (WA) on the IEMOCAP dataset, and 80.25% UA and 79.81% WA on the CREMA-D dataset, outperforming the existing state-of-the-art methods. This study shows that using cross-domain transfer learning is beneficial for low-resource emotion recognition. The enhanced models developed in other domains (for non-emotional tasks) can further improve the accuracy of SER. Full article

A crucial role is played by steering-angle prediction in the control of autonomous vehicles (AVs). It mainly includes the prediction and control of the steering angle. However, the prediction accuracy and calculation efficiency of traditional YOLOv5 are limited. For the control of the steering angle, angular velocity is difficult to measure, and the angle control effect is affected by external disturbances and unknown friction. This paper proposes a lightweight steering angle prediction network model called YOLOv5Ms, based on YOLOv5, aiming to achieve accurate prediction while enhancing computational efficiency. Additionally, an adaptive output feedback control scheme with output constraints based on neural networks is proposed to regulate the predicted steering angle using the YOLOv5Ms algorithm effectively. Firstly, given that most lane-line data sets consist of simulated images and lack diversity, a novel lane data set derived from real roads is manually created to train the proposed network model. To improve real-time accuracy in steering-angle prediction and enhance effectiveness in steering control, we update the bounding box regression loss function with the generalized intersection over union (GIoU) to Shape-IoU_Loss as a better-converging regression loss function for bounding-box improvement. The YOLOv5Ms model achieves a 30.34% reduction in weight storage space while simultaneously improving accuracy by 7.38% compared to the YOLOv5s model. Furthermore, an adaptive output feedback control scheme with output constraints based on neural networks is introduced to regulate the predicted steering angle via YOLOv5Ms effectively. Moreover, utilizing the backstepping control method and introducing the Lyapunov barrier function enables us to design an adaptive neural network output feedback controller with output constraints. Finally, a strict stability analysis based on Lyapunov stability theory ensures the boundedness of all signals within the closed-loop system. Numerical simulations and experiments have shown that the proposed method provides a 39.16% better root mean squared error (RMSE) score than traditional backstepping control, and it achieves good estimation performance for angles, angular velocity, and unknown disturbances. Full article

When a high-speed rolling bearing cage comes into contact with rollers, it experiences random movement due to friction and collision, which significantly impacts the overall performance of the bearing. To further investigate the motion law of cages, a test bench for a 7010C angular-contact bearing cage was constructed. This setup utilized laser sensors to obtain changes in attitude and displacement during operation. After analyzing how cage deflection errors influenced trajectory measurements, corrections were applied to the measurement results. Additionally, an investigation was conducted into the effects of varying rotational speeds on the dynamic performance of the cage. Simulations were performed using ADAMS software, which verified both the effectiveness of the measuring method and the testing results. The findings indicated that within the tested range of rotational speeds, the centroid trajectory stability of the cage gradually improved as rotational speed increased and then began to show a tendency to deteriorate. Furthermore, there existed a negative correlation between the deflection error of the cage and the centroid trajectory stability. Full article

Traditional rehabilitation training for stroke patients with ankle joint issues typically relies on the expertise of physicians. However, when confronted with complex challenges, such as online decision-making or assessing rehabilitation progress, even seasoned experts may not anticipate all potential hurdles. A novel approach is necessary—one that effectively addresses these complexities without solely leaning on expert experience. Previous studies have introduced a rehabilitation assessment method based on fuzzy neural networks. This paper proposes a novel approach, which is a VR-aided ankle rehabilitation decision-making model based on a convolutional gated recurrent neural network. This model takes various inputs, including ankle dorsiflexion range of motion, angular velocity, jerk, and motion performance scores, gathered from wearable motion inertial sensors during virtual reality rehabilitation. To overcome the challenge of limited data, data augmentation techniques are employed. This allows for the simulation of five stages of rehabilitation based on the Brunnstrom staging scale, providing tailored control parameters for virtual training scenarios suited to patients at different stages of recovery. Experiments comparing the classification performance of convolutional neural networks and long short-term memory networks were conducted. The results were compelling: the optimized convolutional gated recurrent neural network outperformed both alternatives, boasting an average accuracy of 99.16% and a Macro-F1 score of 0.9786. Importantly, it demonstrated a strong correlation (correlation coefficient r > 0.9) with the assessments made by clinical rehabilitation experts, showing its effectiveness in real-world applications. Full article

This study investigates the morphological evolution of river particles and their mechanical behavior during sediment transport. River particles exhibit distinct shape differences between upstream and downstream sections, with particles becoming progressively rounded downstream. The rounding process is quantitatively described using morphological indices. The analysis reveals upstream particles are more angular, while downstream particles become increasingly rounded due to erosion and abrasion, modeled by a unified abrasion function. The Loop subdivision method effectively simulates this gradual rounding process. Additionally, the Discrete Element Method (DEM) calculates the natural angle of repose for particles with varying erosion levels, showing angles ranging from 38.2° for angular particles to 34.4° for rounded particles, closely matching field observations. The numerical results effectively demonstrate the interlocking effect caused by particle morphology. This research enhances the understanding of sediment transport dynamics and provides a robust framework for modeling particle shape evolution. Full article