Abstract The ballastless track in a high‐speed railway (HSR) is the primary structure that directly bears the load of high‐speed trains and its dynamic responses directly reflect the operation state of HSRs. However, there are few field test results about spatial dynamic responses of HSR ballastless tracks in previous research. Taking a prototype of an HSR ballastless track as the research object, dynamic response data of the HSR ballastless track were measured by arranging acceleration sensors thereon and applying vibration loads at different positions. The spatial dynamic response laws of the HSR ballastless track were explored in detail. The research results show that in the vertical, transverse, and longitudinal directions, the maximum accelerations all occur near the exciting points; the acceleration in the longitudinal direction is attenuated mainly in the supporting layer and has become very small after being transmitted to the top of the subgrade. The research findings provide guidance for the monitoring of dynamic responses of HSR ballastless tracks.

Analysis of balance in adults with chronic axial spinal pain receiving medial branch block or radiofrequency ablation procedures.

ABSTRACTWildlife behavior studies have provided vital information towards understanding the natural histories of wildlife species and identified crucial components regarding their habitat and metabolic needs. For many species, typical behavioral data are collected using diurnal scans that have limitations in both when and where the data can be collected, ultimately leading to biases in behavioral patterns. With technological and analytical advancements of radiotechnology, behavior data can be collected more often and over larger spatial scales than with traditional methods. This study compares the behavioral time budget estimates between two different observational methods: ground‐truthed diurnal scanning observations and 24‐h tri‐axial accelerometer (ACC) GPS/GSM transmitter data that were classified using machine learning. We used the time budgets produced from the two methodologies and calculated the daily energy expenditure (DEE) for wintering Lesser Scaup (Aythya affinis) to explore the implications of biased behavioral data. We found significantly more feeding and less flight behavior of birds in the ACC data than in the visual scanning data. Using the ACC behavior proportions of the two most energetically demanding behaviors (feeding and flying), we found that feeding occurred 42% more during the day and flying occurred 23% more during the night. Lastly, we identified that the DEE estimated using the diurnal scanning observations produced a significantly lower estimate than with the 24‐h ACC data. This advanced way of interpreting wildlife behavior patterns can increase our understanding of wildlife species' natural history and make improved decisions regarding wildlife conservation and management. Incorporating this new technique of wildlife behavioral observations, we provided a new framework to expand our current knowledge of wintering waterfowl behaviors and energetic needs that can be adapted to research the vast intricacies of wildlife behavior.

Purpose This paper aims to propose an approach to substantially improve vibration reduction in spur gear pairs by using damping particles in conjunction with supplementary lubricant. Design/methodology/approach The effectiveness of the proposed approach is experimentally evaluated via the vibration signal of a gearbox. First, the experimental device is designed for spur gear pairs with a speed ratio of 1:1. Both driving and driven gears have six technology holes for containing the damping particles. In addition, the particle filling rate is maintained at 50% of the hole volume. In the case of adding lubricant, BESLUX GEAR-ATOX 320 or BESSIL F-100 oil is supplemented to the holes in steadily increasing volumes. The Simcenter SCADA system collects the vibration signal of the gearbox from the acceleration sensor and exports the data for the Simcenter TESTLAB software to analyze the signal. Findings The experimental results indicate that the root mean square (RMS) value of radial vibration can be reduced up to 16.05% when using only the damping particles. Especially, adding lubricant can further diminish the radial vibration’s RMS value by up to 61.65% compared to using the damping particles without lubricant. Furthermore, selecting a suitable viscosity of the lubricant can further enhance the vibration reduction. Originality/value The significant vibration reduction efficiency of the proposed method, which integrates damping particles with inter-particle lubricant, is experimentally validated for the spur gear pair transmission. This study is expected to provide an effective approach to improve the vibration response of gearbox systems. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2025-0314/

Abstract Accurate monitoring of the Earth's gravity field is crucial for understanding mass redistribution processes related to climate change, hydrology, and geodynamics. The Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow‐On (GRACE‐FO), have provided invaluable satellite gravimetry data through low‐low satellite‐to‐satellite tracking (LL‐SST). However, the precision of gravity field recovery is significantly affected not only by data gaps in the accelerometer (ACC) measurements, but also by potential failures or limitations in their performance. To mitigate these issues, accelerometer data transplantation has been employed, leveraging the similarity in non‐gravitational accelerations experienced by both satellites. This study presents an in‐depth assessment of transplant noise and evaluates advanced accelerometer configurations, including Cold Atom Interferometry (CAI) accelerometers and hybrid electrostatic‐quantum accelerometer setups for future satellite gravimetry missions. Through closed‐loop LL‐SST simulations, we compare four different accelerometer configurations, ranging from conventional electrostatic accelerometers (EAs) to fully hybrid CAI‐EA setups. Results indicate that a dual hybrid accelerometer configuration offers the highest accuracy in gravity field recovery, while a transplant‐based hybrid approach significantly enhances the performance of non‐gravitational force modeling without requiring additional instrumentation. The findings underscore the potential of quantum accelerometery and transplant methodologies for future satellite gravimetry missions, offering a cost‐effective solution to improve gravity field recovery, while benefitting from new sensor types.

This article aims to describe a novel workflow designed for generating a new family of minimal surface-based lattice structures with improved performance in terms of material budget compared to the well-known cells like Gyroid and Schwartz. The implemented method is based on the iterative resolution of a dynamic model, where proper forces are applied to generate minimal surface lattices, considering the boundary conditions and the constraint configurations. The novelty of the approach is given by the ability to create a minimal surface without resolving the partial differential equation and without knowing the exact minimal surface generative function. The starting geometry used for the lattice generation is the hypercube, parametrized to create different lattice configurations. Creating five different starting geometries and two constraint configurations, ten different lattice cells were created. For the comparison, a representative parameter of the material budget has been introduced and used to define the two best cells. The material budget is crucial for particle accelerator components, sensors, and detectors. These cells have been compared with Gyroid and Schwartz of the same thickness and bounding box, highlighting improvements of a factor of 2.3 and 1.7, respectively, in terms of material budget. The same cells have also been 3D-printed and tested under compression, and the obtained force–displacement curves were compared with those from a finite element analysis, demonstrating good agreement in the elastic region.

The temporal degradation of mechanical performance in large-span bridges necessitates the precise updating of Finite Element model parameters to guarantee accurate safety assessments and service life predictions. However, existing deep learning-based updating methodologies predominantly rely on single-physical-field inputs and assume homogeneous data topologies, thereby failing to capture complex, high-order mechanical interactions across heterogeneous physical domains. To overcome these limitations, this study proposes the Integrating Multi-Physical-Field Encoding Heterogeneous Graph Neural Network (IMPFE-HGNN). This novel architecture explicitly models the heterogeneous topology among strain, deflection, temperature, cable force, and acceleration sensors via meta-path subgraphs and relationship-aware encodings, enabling the extraction of high-order multi-physics semantics inaccessible to traditional architectures. Validated through a case study on a long-span cable-stayed bridge, the IMPFE-HGNN demonstrates substantial efficacy in parameter identification, yielding maximum correction rates of 43.33% for Poisson's ratio and 10.20% for elastic modulus. Consequently, the predictive fidelity of the updated FE model is significantly enhanced: strain prediction error is reduced by a median of 61.4% (peaking at 77%), while deflection prediction accuracy improves by a median of 72.8% (peaking at 87%). Ablation studies substantiate the critical contributions of meta-path subgraphs and relationship encoding mechanisms, while sensitivity analyses determine optimal hyperparameters, identifying a meta-path length of 5 and a feature-mapping dimension of 128. Overall, this study presents a physically interpretable heterogeneous GNN paradigm for multi-source data fusion, offering a robust and precise framework for the structural performance assessment of long-span cable-stayed bridges.

Optimal crop recommendation system development is essential for improving agricultural productivity through the assurance of compatible crop selections with soil and environmental factors. This study uses multi-sensor data collection from the Internet of Things (IoT) and machine learning approaches to design a specific and adaptive crop recommendation system. A publicly available dataset from Kaggle, containing 2,200 samples from 22 crop types with seven key attributes (N, P, K, temperature, humidity, pH, and rainfall), was used. The performance of several machine learning algorithms, such as Random Forest (RF), Decision Tree, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Linear Regression, was compared both with and without hyperparameter tuning using GridSearchCV. The outcome reveals that RF with GridSearchCV optimization achieves an optimal accuracy value of 99.24% for a 70:30 train-test split, outperforming other models in different performance measures and train-test partition ratios. A comparison of inference times further shows that the developed model achieves a satisfactory balance between accuracy and processing times, making it suitable for real-time IoT applications. System implementation and performance evaluation were performed on a Raspberry Pi 4B with a Coral accelerator and soil sensors integrated, thereby demonstrating its potential for real-time implementation. These findings underscore the significance of hyperparameter optimization and data-driven modeling in enhancing precision and versatility in precision agriculture approaches. Future research will aim to investigate larger datasets, reduce noisy sensor outputs, and incorporate additional environmental parameters—such as sunlight intensity and soil salinity—into the system for further improved recommendation accuracy under changing conditions.

Accelerometry is a state-of-the-art procedure to capture physical activity. However, the large variety of accelerometry metrics and wearing positions hamper the comparability of outcomes. Since this is a well-known challenge, we investigated how outcomes can be translated across four metrics and six sensor positions. Twenty healthy adults conducted 32 structured and semi-structured activities while wearing accelerometers at the hip, chest, thigh, wrist, ankle, and upper arm. The raw data was converted into four common metrics: Movement Acceleration Intensity (MAI), Euclidean Norm Minus One (ENMO), Mean Amplitude Deviation (MAD) and counts per minute (CPM), as computed by the Actigraph GT3X+ algorithm. Relationships between acceleration metrics and sensor positions were quantified via Pearson correlations and scatterplots. Our results show that nearby sensor positions were highly correlated (e.g., MAD hip and thigh: r = .96), while correlations between more distant sensor positions were weaker and less linear (e.g., MAD wrist and thigh: r = .80). Correlations between MAI, MAD and ENMO were high (r = .9), while correlations between CPM and other metrics were substantially lower (r = .78), less linear, and influenced by activity type. Thus, linear conversion between MAI, ENMO and MAD are highly feasible, but converting CPM may be less accurate. Linear conversions between nearby sensor positions are accurate, yet linear conversions between more distant sensor positions appear challenging. Importantly, based on 32 activities as well as metric- and sensor-location-specific configurations, we provide a comprehensive overview of outcome measures that enables researchers to individually explore conversion opportunities towards their own data.

This study investigated how unsteady flow conditions influence the swimming physiology and energetic performance of Chinook salmon using co-implanted heart rate (HR) and acceleration (AC) sensors. Fish were monitored for HR, AC, and overall dynamic body acceleration (ODBA) in two experimental settings: (1) controlled swimming at increasing speeds (0.15-0.90 m s⁻¹) in a swim-tunnel under steady and unsteady flow, and (2) free-swimming sentinel fish in tanks under steady and subsequent unsteady flow for two weeks each. In experiment 1, HR remained consistently high (81-84 bpm) across all speeds under both flow conditions, suggesting limited capacity to further elevate cardiac output. MO2 increased from 213±10 to 307±16 and from 225±12 to 330±17 mg kg-1 h-1 under steady and unsteady flow, respectively. AC and ODBA increased linearly with speed and were positively correlated under both flow conditions. In experiment 2, circadian patterns were evident in HR, AC, and ODBA of the free-swimming fish. Fish exhibited higher daytime and nighttime HR and AC under unsteady flow compared to steady flow conditions, while ODBA remained similar. Regression models based on swim-tunnel data accurately predicted AC and ODBA in free-swimming fish, indicating consistent relationships between swimming speed and acceleration dynamics. The higher HR and AC of free-swimming fish under unsteady conditions indicated a 3-5% increased energetic investment. Overall, this study provides insight into how dynamic flow environments shape the physiological responses of Chinook salmon, informing predictions of fish performance in offshore aquaculture systems.

Experimental study on damage evolution characteristics of concrete beams embedded with acceleration sensor under cyclic loading

Micro-nano photomechanical accelerometers have the advantages of small size, high sensitivity, low cost, high reliability, low power consumption, and can be mass-manufactured, and are widely used in automotive, industrial production, aerospace and many other fields. However, the sensitivity of accelerometers is not sufficiently high. The utilization of optical frequency combs can effectively enhance the sensitivity of accelerometers and also improve their precision. Based on the mechanical properties of Si racetrack resonator, a kind of accelerometer with fixed beam structure is designed by using semiconductor micro-nano machining technology. The output signal of accelerometer based on fixed beam structure has a linear relationship with the measured acceleration intensity, and an acceleration sensor structure is proposed. By analyzing the spectral characteristics of the racetrack microring resonator, the acceleration value of the system is measured by using the offset characteristic of the output spectrum under the action of acceleration. Simulation results show that the sensitivity of the system can reach 150 pm /g. In addition, a method based on frequency comb source is proposed, which reduces the uncertainty of resonant frequency difference and improves the accuracy of acceleration determination compared with a single input light source. This structure provides a theoretical basis for the preparation of highly integrated and low-cost micro-acceleration sensors.

The growing demand for sustainable transportation has accelerated research in electric vehicle (EV) technologies, with energy storage systems playing a pivotal role in achieving higher efficiency and reliability. However, lithium-ion batteries, though advanced, face limitations during transient acceleration due to erratic load profiles and high-current peaks, resulting in voltage sag, heating, and accelerated degradation. This paper presents a microcontroller-based hybrid energy storage system (HESS) integrating a lithium-ion battery with a supercapacitor bank to mitigate these issues. The system adaptively manages power flow during acceleration using an ATMEGA328 microcontroller and an acceleration sensor to engage or disengage the supercapacitor via a ULN2003 relay driver. Experimental results demonstrate a 30% reduction in peak battery current and improved voltage and temperature stability, validating the hybrid system’s effectiveness in enhancing battery longevity and overall EV performance

The vibration of the combine harvester header assembly directly affects harvesting efficiency and operational quality. To address the insufficient dynamic characterization of the cantilever conveying trough under complex field excitations, this study systematically analyzes the vibration response characteristics of the header assembly under multi-source coupled excitation through field experiments and theoretical modeling. Acceleration sensors arranged at three measurement points on the header bottom collected vibration data, revealing that the dominant vibration frequency of the header has a deterministic harmonic relationship with the threshing drum’s operating frequency (3rd harmonic on the left side, 1.5th harmonic on the right side), demonstrating dynamic coupling effects within the integrated system. Through acceleration response analysis at four symmetric measurement points on the connection, the external excitation force was quantified as a sinusoidal function correlated to the feed quantity (F = 1094.4 sin(50πt/3)). A damped pendulum model of the cantilever conveying trough was established using the Lagrange method. Validation results show that the error between the predicted steady-state swing amplitude and measured values is only 1.11–4.3%, confirming the effectiveness of this simplified model in characterizing the system’s steady-state response. This research provides a theoretical foundation and methodological support for dynamic characterization, parameter optimization, and stability control of the cantilever header system in combine harvesters.

Abstract As the largest component of Earth's time‐variable gravity field, is crucial for understanding regional mass variability and climate changes. However, the estimated by Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO) is currently unreliable and has been replaced by values derived from the satellite laser ranging (SLR) to ensure the proper scientific application of GRACE‐FO data products. Yet, exploring the reasons for the bias in the estimates has been of great interest, as there is always a concern about the inconsistency between the SLR and GRACE solutions. This study investigates the impact of accelerometer (ACC) data processing methods on the accuracy of estimation by examining the anomalous response characteristics of the GRACE‐FO ACC to the thruster. Firstly, using the statistical analysis method, we identify a correlation between the anomalous response caused by the Roll thrusters and the ACC range mode. From this basis, we make a conjecture about the mechanism of anomalous response generation, and a new thruster firing physical model based on statistical results is proposed to replace the anomalous response and optimize the current ACC data processing flow. From the results of the gravity field recovery, the corrected ACC data improve the accuracy of the estimation of . After multiple months of estimation, the estimate RMSD between the new ACC product and the TN‐14 solutions was , better than the precision of the estimate of the reference solutions. Finally, we discuss the correlation between this ACC processing method and the accuracy of estimation.

Abstract To investigate the liquefaction pattern of undisturbed loess during seismic activity, a vibration table model has been developed to mimic the seismic response of loess slopes. The research object of this experiment is large-sized undisturbed loess, and four undisturbed loess samples with various moisture contents and slope shapes were set up. Meanwhile, pore water pressure sensors and acceleration sensors are installed in the soil to monitor the response of various measuring points under earthquake action. The results showed that in terms of pore water pressure response, the pore water pressure time history curves of each sample showed a highly consistent trend, and the coefficient of variation of data from different measurement points was less than 8%, indicating that the sensor monitoring system was stable and reliable. The pore water pressure ratio of all samples at a depth of 100 mm exceeded the liquefaction critical value of 0.7, verifying the common law that shallow loess was prone to liquefaction. Sample 4 (high moisture content+steep slope) had a pore water pressure ratio of 0.82 ± 0.03 at a depth of 370 mm, while Sample 1 (low moisture content+no slope) had only 0.23 ± 0.04 at a depth of 450 mm, reflecting the quantitative influence of moisture content and slope on liquefaction degree. The peak acceleration of all samples was lower than the input of 3.8 m s −2 , and the attenuation law with depth was consistent, indicating that the mechanical mechanism of soil liquefaction leading to loss of bearing capacity was accurately reflected in the experiment. In the linkage verification of triaxial test and vibration table test, the peak pore water pressure ratio of samples 2 and 4 reached 0.7 at the liquid limit water content, which was consistent with the result of complete liquefaction above the liquid limit. The liquefaction degree of undisturbed loess is directly proportional to the moisture content of the loess soil and the slope gradient. Additionally, it demonstrates that the vibration table test can effectively simulate and analyze the liquefaction behavior of loess under earthquake action, providing important theoretical basis for seismic design and liquefaction disaster prevention in loess areas.

Epilepsy patients commonly report stress as a frequent seizure trigger; however, the objective seizure-stress relationship is unclear due to self-report biases and difficulty in objective quantification of stress. This work presents a dataset from twenty epilepsy patients undergoing cognitive stress elicitation protocols, participating in laboratory experiments with computer-based tasks at predefined difficulty levels, and in situational experiments by independently choosing tasks with at least two difficulty levels. Physiological signals from wearable electroencephalography, photoplethysmography, acceleration, electrodermal activity, and temperature sensors were recorded. The task-related perceived cognitive stress was collected using two 5-point Likert scales of self-reported mental workload and stress, contrasted by a pairwise NASA-TLX questionnaire. Additionally, the dataset includes a patient-reported list of seizure-provoking and -inhibiting factors. Results illustrated individual and heterogeneous responses to cognitive tasks, with some modalities yielding statistically significant features, while others demonstrated expected directional trends. The findings support the validity and suitability of the proposed dataset for cognitive stress detection and the potential to map seizure-related factors to cognitive stress events.

<div>If road friction coefficient can be measured in a car driving, the performance of advanced driver-assistance systems (ADAS) such as antilock braking system (ABS) and automatic braking systems can be improved. Generally, ADAS uses information obtained from wheel speed sensors, acceleration sensors, and the like. However, it is difficult to measure accurately road friction coefficients with these sensors. Therefore, many studies measured road friction coefficients from strain or deformation in the bottom of a tire (tread), which is the only place to contact with a road surface. However, a sensor installed on the bottom of a tire is easy to peel or damage because greater deformation occurs locally on the bottom of a tire. Therefore, this study develops a method of measuring the road friction coefficient from the strain induced in a tire sidewall. If the tire sidewall can be used, stable measurement can be expected because the sidewall is harder to deform locally than the bottom of a tire. It has be previously confirmed that the triaxial direction loads acting on a ground contact surface of a tire and the strain induced in the tire sidewall have almost a linear relationship. By determining the experimental formulas about the relationship, we can measure road friction coefficient during car driving. This article describes the method to determine appropriate formulas with determining the optimal measurement condition of the strains induced in the tire sidewall and confirms the availability with actual driving experiments.</div>

The adoption of active upper-limb prostheses with multiple degrees of freedom is largely lagging due to bulky designs and counterintuitive operation. Accurate gesture prediction with minimal sensors is key to enabling low-profile, user-friendly prosthetic devices. Wearable sensors, such as electromyography (EMG) and accelerometry (ACC) sensors, provide valuable signals for identifying patterns relating muscle activity and arm movement to specific gestures. This study investigates which sensor type (EMG or ACC) has the most valuable information to predict hand grasps and identifies the signal features contributing the most to grasp prediction performance. Using an open-source dataset, we trained two types of subject-specific classifiers (LDA & KNN) to predict 10 grasp types in 13 individuals with and 28 individuals without amputation. Having 4-fold cross-validation, LDA average accuracies using ACC only features (84.7%) were similar to combined ACC & EMG (88.3%) and much greater than with only EMG features (58.1%). Feature importance analysis showed that participants with amputation reached more than 80% accuracy using only three features, two of which were ACC-derived, while able-bodied participants required nine features, with greater reliance on EMG. These findings suggest that ACC is sufficient for robust grasp classification in individuals with amputation and can support simpler, more accessible prosthetic designs. Future work should focus on incorporating object and grip force detection alongside grasp recognition and testing model performance in real-time prosthetic control settings.

Resin transfer molding (RTM) is a production technique for fiber reinforced polymer components (FRPCs). It allows the embedding of sensors into components that enable production and structural health monitoring. Since void content is one of the most critical quality criteria, this study investigates the capability of embedded acceleration sensors to capture relevant information during RTM monitoring. For this purpose, a local void content classification based on sensor signals was considered, employing Machine learning algorithms for data labeling and classification. The final classifier’s performance was estimated using nested Cross-Validation (nested CV) and a hold-out test set. While the mean CV-estimates exceeded values over 89% for the main measures F 1.14 , F 1 , Recall and Precision, the overall performance on the hold-out set decreased to F 1.14 = 79%, F 1 = 78%, Recall = 88% and Precision = 70%. Model analysis revealed that the small, imbalanced data set led to instability, and that generalization errors from labeling affected the classifier. Nonetheless, class structure was visible in the frequency domain, confirming that the sensors recorded void content information. Furthermore, the main limitations can be addressed by transitioning from laboratory-scale to series production. This study hence provides a foundation for quality control of acceleration sensor embedded FRPCs produced via RTM.