Slow Speed Research Articles

Actuation Performance and Versatility of Photothermally Driven Organic Crystals

Photomechanical crystals exhibit mechanical motion upon light irradiation and may thus find applications as actuators. Over the last decades, many photomechanical organic crystals have been developed, commonly via photochemical reactions, particularly photoisomerization. However, photochemical crystal actuation is associated with several drawbacks, including a limited number of available crystals, slow actuation speed (< 5 Hz), and narrow wavelength range (< 550 nm). Such constraints have hindered the widespread use of crystals as actuation materials. In this minireview, we focus on crystal actuation by employing more universal physical phenomena (the photothermal effect and photothermally resonated natural vibration) and quantitatively evaluate actuation performance. Both mechanisms, particularly the latter, outperformed conventional photomechanical crystal activation in terms of both speed (maximum: 1,350 Hz) and the useful wavelength range (ultraviolet to near‐infrared). The oscillation frequencies of the crystals exceeded those of polymers, efficiently filling the gap between soft and hard materials. Both the photothermal effect and natural vibration can actuate any crystal that absorbs light. These two versatile physical actuation mechanisms could expand 40 years of research on photomechanical crystals—which had been based on photochemical reactions—from the realm of chemistry into engineering and lead to their practical applications in actuators and soft robots.

Librarians’ Awareness and Utilization of Open Access Electronic Journals in Nigerian University Libraries

ABSTRACT This study investigated the awareness, utilization, and challenges associated with Open Access electronic journals among librarians in selected university libraries in Ogun State, Nigeria. A descriptive survey research design was employed, and data were collected through a self-developed questionnaire administered to 95 professional librarians. The findings revealed a moderate level of awareness and utilization of certain Open Access journals, with resources like Library Philosophy and Practice, Library and Information Research, and Information Technology and Libraries being among the most widely known and utilized. However, challenges such as power failures, slow internet speed, and the overwhelming number of available resources hindered effective utilization. The study recommends improving IT infrastructure, raising awareness, fostering collaborations, and implementing user-friendly search interfaces to enhance the utilization of Open Access electronic journals.

Improved ant colony algorithm for path planning based on pheromone difference distribution strategy

In view of the problems of blind search in the initial stage, slow convergence speed and easy to fall into local optimum when the traditional ant colony algorithm is used for mobile robot path planning, an improved ant colony algorithm is proposed. Firstly, according to the distance between each node and the line connecting the starting point and the target point, the initial pheromone is unevenly distributed to make it normally distributed, which reduces the blindness of the algorithm search in the initial stage and speeds up the search for the optimal solution; secondly, the volatility factor is improved, and the principle of double volatility factor is adopted to control the volatility of pheromone, which not only reduces the possibility of local optimum but also speeds up the convergence speed; the redundant path is further optimized to make the path better. Simulation results show that the improved ant colony algorithm in this paper converges faster and more stably than the traditional ant colony algorithm and other improved ant colony algorithms.

Gorilla algorithm based on double random perturbation and its engineering application

Aiming at the problems of artificial gorilla troop optimization algorithm, such as easy to fall into local optimum, slow convergence speed and low optimization accuracy, an artificial gorilla troop optimization algorithm based on double random perturbation strategy is proposed. Firstly, the Halton sequence is introduced to initialize the population to increase the diversity of the population; secondly, a multi-dimensional random number strategy is used in the algorithm optimization stage and an adaptive position search mechanism is proposed in the exploration stage to improve the convergence speed of the algorithm; thirdly, a double random perturbation strategy is proposed to solve the group effect of gorillas and enhance the ability of the algorithm to jump out of the local optimum; finally, a dimension-by-dimensional update strategy is adopted to update individual positions, which improves the convergence accuracy of the algorithm. Through the comparison of the optimization results of 10 benchmark test functions and the Wilcoxon rank sum test, it can be seen that the improved algorithm has greatly improved the optimization accuracy and convergence speed. In addition, through the experimental comparative analysis of an engineering optimization problem, the superiority of the proposed algorithm in dealing with real engineering problems is further verified.

Improved krill swarm algorithm and its application in structural optimization

This paper improves and perfects the standard Krill herd algorithm (KH), which has the defects of slow convergence speed, insufficient calculation accuracy and easy to fall into local optimal solution for complex problems. An improved Krill herd algorithm (SDEKH) integrating improved differential evolution operator and S-type adaptive inertia weight is proposed. SDEKH, KH and other intelligent algorithms are compared and tested through a variety of standard test functions to verify the excellent performance of SDEKH; SDEKH is used to optimize the design of truss structure. By comparing the optimization results with other methods, it is verified that the optimization efficiency and accuracy of SDEKH are improved, which provides a more efficient and accurate method for the optimization design of engineering structures.

DGRO: Doppler Velocity and Gyroscope-Aided Radar Odometry

A stable and robust odometry system is essential for autonomous robot navigation. The 4D millimeter-wave radar, known for its resilience in harsh weather conditions, has attracted considerable attention. As the latest generation of FMCW radar, 4D millimeter-wave radar provides point clouds with both position and Doppler velocity information. However, the increased uncertainty and noise in 4D radar point clouds pose challenges that prevent the direct application of LiDAR-based SLAM algorithms. To address this, we propose a SLAM framework that fuses 4D radar data with gyroscope readings using graph optimization techniques. Initially, Doppler velocity is employed to estimate the radar’s ego velocity, with dynamic points being removed accordingly. Building on this, we introduce a pre-integration factor that combines ego-velocity and gyroscope data. Additionally, leveraging the stable RCS characteristics of radar, we design a corresponding point selection method based on normal direction and propose a scan-to-submap point cloud registration technique weighted by RCS intensity. Finally, we validate the reliability and localization accuracy of our framework using both our own dataset and the NTU dataset. Experimental results show that the proposed DGRO system outperforms traditional 4D radar odometry methods, especially in environments with slow speeds and fewer dynamic objects.

Gait speed after mild stroke/transient ischemic attack was associated with long-term adverse outcomes: A cohort study.

The association between gait speed and adverse outcomes after stroke has not been fully illustrated. This study aimed to explore the association of gait speed on long-term outcomes in minor stroke or transient ischemic attack (TIA). We performed a longitudinal study with acute minor stroke or TIA based on a subgroup of the Third China National Stroke Registry data. The gait speed was evaluated using a 10-meter walking test at discharge and 3 months after the stroke onset. The primary outcomes were poor functional outcomes at 1 year, defined by a modified Rankin Score (mRS) of 2-6. Additional outcomes included all-cause death, ambulate dependency (mRS score 4-6), cognitive impairment (Montreal Cognitive Assessment <26), stroke recurrence, and composite vascular events. The study sample included a total of 1542 stroke patients with a median age of 60 (53-68). At 1-year follow-up, 140 (9.20%) patients experienced poor functional outcomes. Faster gait speed at discharge was associated with lower incidence of poor functional outcome (OR = 0.89; 95% CI, 0.84-0.94), cognitive impairment (OR = 0.93; 95% CI, 0.89-0.96), ischemic stroke recurrence (HR = 0.92; 95% CI, 0.87-0.98), and composite vascular events (HR =0.94; 95% CI, 0.89-0.99) at 1 year. Faster gait speed at 3 months was associated with lower incidence of poor functional outcome (OR = 0.90; 95% CI, 0.85-0.95), ambulate dependency (OR = 0.86; 95% CI, 0.77-0.97), and cognitive impairment (OR = 0.92; 95% CI, 0.88-0.95) at 1 year. Our findings indicated that slow gait speed after minor stroke or TIA may be an independent predictor for long-term poor outcomes. Gait speed may be considered as a vital sign during follow-up in post-stroke patients.

Enhancing Federated Learning in Heterogeneous Internet of Vehicles: A Collaborative Training Approach

The current Internet of Vehicles (IoV) faces significant challenges related to resource heterogeneity, which adversely impacts the convergence speed and accuracy of federated learning models. Existing studies have not adequately addressed the problem of resource-constrained vehicles that slow down the federated learning process particularly under conditions of high mobility. To tackle this issue, we propose a model partition collaborative training mechanism that decomposes training tasks for resource-constrained vehicles while retaining the original data locally. By offloading complex computational tasks to nearby service vehicles, this approach effectively accelerates the slow training speed of resource-limited vehicles. Additionally, we introduce an optimal matching method for collaborative service vehicles. By analyzing common paths and time delays, we match service vehicles with similar routes and superior performance within mobile service vehicle clusters to provide effective collaborative training services. This method maximizes training efficiency and mitigates the negative effects of vehicle mobility on collaborative training. Simulation experiments demonstrate that compared to benchmark methods, our approach reduces the impact of mobility on collaboration, achieving large improvements in the training speed and the convergence time of federated learning.

Lightweight rail surface defect detection algorithm based on an improved YOLOv8

Existing deep learning methods for rail surface defect detection face issues such as poor compatibility with embedded detection systems, high computational resource consumption, and slow detection speeds. To address these challenges, we propose a lightweight rail surface defect detection algorithm11Code: https://github.com/haichao67/GD-YOLOv8. based on an improved YOLOv8.Inspired by Huawei’s Gold-YOLO, our algorithm redesigns the neck architecture, integrates feature extraction, fusion, and injection modules, and replaces Complete Intersection over Union with Scylla Intersection over Union. Comparative experiments confirm its efficacy, achieving 94.9% precision and 81.9% recall on the Rail Surface Defect Dataset, a 26.3% increase in frames per second, and a model size reduction to 63%. Our algorithm not only ensures satisfactory detection results but also reduces the number of model parameters and increases the detection speed.

Color detection of printing based on improved superpixel segmentation algorithm

We propose an improved superpixel segmentation algorithm based on visual saliency and color entropy for online color detection in printed products. This method addresses the issues of low accuracy and slow speed in detecting color deviations in print quality control. The improved superpixel segmentation algorithm consists of three main steps: Firstly, simulating human visual perception to obtain visually salient regions of the image, thereby achieving region-based superpixel segmentation. Secondly, adaptively determining the superpixel size within the salient regions using color information entropy. Finally, the superpixel segmentation method is optimized using hue angle distance based on chromaticity, ultimately achieving a region-based adaptive superpixel segmentation algorithm. Color detection of printed products compares the color mean values of post-printing images under the same superpixel labels, outputting labels with color deviations to identify areas of color differences. The experimental results show that the improved superpixel algorithm introduces color phase distance with better segmentation accuracy, and combines it with human visual perception to better reproduce the color information of printed materials. Using the method described in this article for printing color quality inspection can reduce data computation, quickly detect and mark color difference areas, and provide the degree of color deviation.

Uncertainty quantification on small angle x-ray scattering measurement using Bayesian deep learning

Small angle x-ray scattering (SAXS) is a widely recognized solution for measuring complex nanostructures. With the increasing demand for accurately assessing structural characteristics and optimizing manufacturing processes, uncertainty quantification in SAXS inverse problems has become a critical issue. However, traditional methods face challenges such as slow computation speed and inaccurate estimation of multidimensional parameters. To overcome these issues, we propose an uncertainty quantification approach suitable for SAXS measurement that approximates the posterior using Bayesian deep learning. The effectiveness and reliability of our method are illustrated by assessing structural parameters of synthetic 2D Si grating samples. The uncertainty quantification takes only about 2.3 s, thousands of times faster than the conventional Markov Chain Monte Carlo (MCMC) methods. Also, our method has superior repeatability for parameter measurement compared to the MCMC approaches. It provides the potential of efficient and reliable SAXS measurement in increasingly intricate semiconductor manufacturing.

YOLO-LFPD: A Lightweight Method for Strip Surface Defect Detection.

Strip steel surface defect recognition research has important research significance in industrial production. Aiming at the problems of defect feature extraction, slow detection speed, and insufficient datasets, YOLOv5 is improved on the basis of YOLOv5, and the YOLO-LFPD (lightweight fine particle detection) model is proposed. By introducing the RepVGG (Re-param VGG) module, the robustness of the model is enhanced, and the expressive ability of the model is improved. FasterNet is used to replace the backbone network, which ensures accuracy and accelerates the inference speed, making the model more suitable for real-time monitoring. The use of pruning, a GA genetic algorithm with OTA loss function, further reduces the model size while better learning the strip steel defect feature information, thus improving the generalisation ability and accuracy of the model. The experimental results show that the introduction of the RepVGG module and the use of FasterNet can well improve the model performance, with a reduction of 48% in the number of parameters, a reduction of 13% in the number of GFLOPs, an inference time of 77% of the original, and an optimal accuracy compared with the network models in recent years. The experimental results on the NEU-DET dataset show that the accuracy of YOLO-LFPD is improved by 3% to 81.2%, which is better than other models, and provides new ideas and references for the lightweight strip steel surface defect detection scenarios and application deployment.

Thermal features and its effect on the properties of AISI 4140 fabricated by conventional and extreme high-speed laser material deposition

The extreme high-speed laser material deposition (EHLA) process has the potential to enable additive manufacturing for mass production by overcoming the limitations of slow scanning speed in conventional laser material deposition (LMD) processes. Thermal features are the key factors to link process and properties. A sound understanding of process-thermal-property relationships is essential for performance control and process optimization of a deposited component. In this study, we studied the thermal characteristics within a single layer and among layers for continuous processing using the numerical method, and reveal the mechanism of microstructure and hardness changes of AISI 4140 material formed by EHLA and LMD processes through the analysis of thermal properties and temperature history results. The grain size and hardness evolution for both processes during single-layer cladding and continuous forming processes were investigated. The results revealed that the grain refinement effect in continuous LMD processing is stronger than that in EHLA process (from 30.0 μm for single layer to 3.2 μm for multi layers vs. from 18.6 μm for single layer to 2.2 μm for multi layers). Similar hardness values were obtained by LMD and EHLA processes, with mean values of 511 HV and 472 HV, respectively. The yield and tensile strengths of EHLA were superior to the conventional cast material, but inferior to those of the conventional material quenched and tempered at lower temperatures. The enhanced tensile results of EHLA process were found similar to those prepared through conventional method with quench and tempering at 600 °C.

Improving the Kepler optimization algorithm with chaotic maps: comprehensive performance evaluation and engineering applications

The Kepler Optimisation Algorithm (KOA) is a recently proposed algorithm that is inspired by Kepler’s laws to predict the positions and velocities of planets at a given time. However, although promising, KOA can encounter challenges such as convergence to sub-optimal solutions or slow convergence speed. This paper proposes an improvement to KOA by integrating chaotic maps to solve complex engineering problems. The improved algorithm, named Chaotic Kepler Optimization Algorithm (CKOA), is characterized by a better ability to avoid local minima and to reach globally optimal solutions thanks to a dynamic diversification strategy based on chaotic maps. To confirm the effectiveness of the suggested approach, in-depth statistical analyses were carried out using the CEC2020 and CEC2022 benchmarks. These analyses included mean and standard deviation of fitness, convergence curves, Wilcoxon tests, as well as population diversity assessments. The experimental results, which compare CKOA not only to the original KOA but also to eight other recent optimizers, show that the proposed algorithm performs better in terms of convergence speed and solution quality. In addition, CKOA has been successfully tested on three complex engineering problems, confirming its robustness and practical effectiveness. These results make CKOA a powerful optimisation tool in a variety of complex real-world contexts. After final acceptance, the source code will be uploaded to the Github account: nawal.elghouate@usmba.ac.ma.

Analysis of electrode locations on limb condition effect for myoelectric pattern recognition

BackgroundGesture recognition using surface electromyography (sEMG) has garnered significant attention due to its potential for intuitive and natural control in wearable human–machine interfaces. However, ensuring robustness remains essential and is currently the primary challenge for practical applications.MethodsThis study investigates the impact of limb conditions and analyzes the influence of electrode placement. Both static and dynamic limb conditions were examined using electrodes positioned on the wrist, elbow, and the midpoint between them. Initially, we compared classification performance across various training conditions at these three electrode locations. Subsequently, a feature space analysis was conducted to quantify the effects of limb conditions. Finally, strategies for group training and feature selection were explored to mitigate these effects.ResultsThe results indicate that with the state-of-the-art method, classification performance at the wrist was comparable to that at the middle position, both of which outperformed the elbow, consistent with the findings from the feature space analysis. In inter-condition classification, training under dynamic limb conditions yielded better results than training under static conditions, especially at the positions covered by dynamic training. Additionally, fast and slow movement speeds produced similar performance outcomes. To mitigate the effects of limb conditions, adding more training conditions reduced classification errors; however, this reduction plateaued after four conditions, resulting in classification errors of 22.72%, 22.65%, and 26.58% for the wrist, middle, and elbow, respectively. Feature selection further improved classification performance, reducing errors to 19.98%, 19.75%, and 27.14% at the respective electrode locations, using three optimal features derived from single-condition training.ConclusionsThe study demonstrated that the impact of limb conditions was mitigated when electrodes were placed near the wrist. Dynamic limb condition training, combined with feature optimization, proved to be an effective strategy for reducing this effect. This work contributes to enhancing the robustness of myoelectric-controlled interfaces, thereby advancing the development of wearable intelligent devices.

Application of SPNGO-VMD-SVM in rolling bearing fault diagnosis

Abstract Traditional bearing fault feature extraction and fault classification methods have low recognition accuracy and limited recognition capability in noisy environments. To address this problem, this paper proposes an improved northern eagle algorithm (SPNGO) to optimize the variational modal decomposition (VMD) and support vector machine (SVM) to achieve bearing fault diagnosis. Firstly, to overcome the disadvantages of NGO, such as easy fall into local optimal solutions and slow convergence speed, the Sine Cosine Strategy (SCA) and Position Optimisation Search Algorithm (POS) are introduced to optimize the NGO, and the superiority of the SPNGO algorithm is proved through the comparison of different algorithms. Then, SPNGO-VMD is used to adaptively decompose the vibration signals of faulty bearings and generate multiple modal components IMF. The effective IMF components are screened based on the craggy principle to reconstruct the signals. Finally, the reconstructed feature signals are input into SPNGO-SVM for fault classification and compared with other fault diagnosis models. The study results show that the fault diagnosis accuracy of SPNGO-VMD-SVM can reach 96.67%, which can effectively improve the accuracy of bearing fault diagnosis.&amp;#xD;

Research on the Performance and Control Strategy of Electro-Hydraulic Servo System for Selective Hole Digging Tree Planter

Compared to agricultural environments, afforestation sites are more complex, often presenting issues such as undulating and uneven terrain. These conditions lead to instability in hole digging depth and plant spacing during continuous movement, and the hole shape may not meet expectations. Additionally, the hydraulic system exhibits slow response speed and long steady-state time, affecting the quality of sapling planting. To address these issues, this paper designs an intelligent planting control system for intermittent hole digging under continuous dynamic movement, based on a large tree planter. The focus is on studying the dynamic accuracy of the hole digging cylinder to resolve the instability of plant spacing and planting depth in actual planting processes. Firstly, a motion trajectory model of the intermittent hole digging mechanism is established to obtain the relationship between the displacement trajectory of the rotating cutter and the displacements of the floating cylinder and the hole digging cylinder. Secondly, a mathematical model of the electro-hydraulic servo system is established to control the dynamic accuracy of the hole digging operation. Finally, a Simulink simulation model of the system is established to analyze the performance indicators of the hydraulic system during operation using step and sinusoidal excitation signals. The test results show that the displacement of the hydraulic piston rod can ensure a linear extension trend within the range of 0 to 0.4 m, and the extension distance of the hole digging cylinder in the planting system is 0 to 0.35 m, ensuring linear change within this stroke. When the system’s extension command is 1 V, the actual output is 0.6 m, with a relative error of less than 10% compared to the simulation value, indicating that the control strategy can effectively improve the dynamic performance of the system. When the hydraulic system is in a steady-state extension state at 50 to 58.6 s, the relative error with the simulation value is 7.3%, meeting the “double ten indicators” requirement. The research results clearly verify the superior performance of the proposed intelligent control system, and the proposed control strategy has great potential in practical applications, promising to improve afforestation quality by stabilizing planting spacing and planting depth.

Migratory behaviour of Ipswich Sparrows during spring

Optimal migration theory predicts birds will follow migration strategies that minimize time, energy, or predation risk, and these strategies employ specific and conflicting behaviours. Many behaviours driving songbird strategies are relatively well known, however gaps remain, especially for spring migration. Here, we describe the behaviours of a short-distance migrant, the Ipswich Sparrow ( Passerculus sandwichensis princeps Maynard, 1872), during spring migration. We determined the start and end dates of migration, migratory duration, travel speed, distance per night, and stopover location and duration. We also examined whether behaviours differed by sex and age. Sparrows migrated between March and June, and took an average of 29.1 days at a speed of 7.2 ± 9.4 m/s (SD) to fly ∼1574 km to their breeding grounds. Sparrows flew approximately 100 km/night and made stopovers of &gt;7 days adjacent to ecological barriers. Males migrated before females, and behaviour did not differ with age. Overall, Ipswich Sparrows had a relatively slow travel speed and covered short distances each night consistent with efforts to minimize energy costs or predation risk. They had prolonged stopovers suggesting that they were also trying to minimize time. More work is required to determine which strategy sparrows may be using during spring migration.

The Difference in Achilles Tendon Loading within Immobilizing Boots Based on Ankle Angle, Boot Type, and Walking Speed.

Achilles tendon rupture is an increasingly common injury treated with progressive rehabilitation in an immobilizing boot. However, it is poorly understood how ankle angle, boot type, and walking speed affect Achilles tendon loading. These different parameters would affect Achilles tendon loading in terms of (from greatest to least) ankle angle constraint, immobilization style, boot construction, and walking speed. Descriptive laboratory study. Ten healthy young adults (8 women and 2 men; age, 21 ± 2 years; body mass index, 21.5 ± 3.0 kg/m2) walked in 3 different immobilizing boots at self-selected slow, medium, and fast walking speeds. The authors estimated Achilles tendon loading using a 3-part instrumented insole within the immobilizing boot. The authors averaged tendon load across every stride for each condition and calculated 2-sided bootstrap confidence intervals. Peak tendon loading was compared across all boots, ankle angles, and walking speeds. All boots and immobilization styles decreased tendon loading with respect to shod walking. Immobilization angle had the largest effect on tendon loading, followed by boot construction, and finally walking speed. Ankle angle, boot type, and walking speed can be modified to change loading progression during rehabilitation. Understanding how immobilization affects tendon loading will enable clinicians to modify rehabilitation to improve functional outcomes.

Super-resolution lensless on-chip microscopy based on array illumination and scattering multiplexing with polystyrene microspheres layer

The resolution of lensless on-chip microscopy is mainly limited by the pixel size of the image sensor. Many super-resolution techniques have emerged to solve the problem of insufficient imaging resolution. Most existing pixel super-resolution technologies rely on precise electric translation stage for hundreds of high-precision displacements, or on expensive tunable lasers to generate diffraction diversity. Conventional ptychography iterative engine (PIE) is considered an effective method for improving imaging resolution, but it is prone to oscillations in the early stage of iteration. In this paper, we propose a ptychography imaging technique based on scattering multiplexing, which involves coating the surface of the image sensor with a layer of polystyrene microspheres, and utilizing a 4 × 3 LED array to sequentially illuminate the sample. An innovative ptychography reconstruction algorithm based on dual amplitude gradient descent (DAGD) is designed for the reconstruction from the holograms, which effectively avoids the problems of slow convergence speed and obvious oscillation. Compared with other similar technologies, our system has no moving parts and uses inexpensive partially coherent light illumination. It only records 12 holograms and reaches the imaging resolution of 1.23 μm, which is 1.36 times pixel super-resolution compared with the pixel size of the sensor.