Search Results (16,538)

The far-infrared absorption spectrum of monodeuterated water vapor, HD¹⁶O, is analyzed using three high-sensitivity absorption spectra recorded by high-resolution Fourier transform spectroscopy at the SOLEIL synchrotron facility. The gas sample was obtained using a 1:1 mixture of H₂O and D₂O leading to a HDO abundance close to 50%. The room temperature spectra recorded in the 50–720 cm⁻¹ range cover most of the rotational band. The sensitivity of the recordings allows for lowering by three orders of magnitude the detectivity threshold of previous absorption studies in the region. Line centers are determined with a typical accuracy of 5 × 10⁻⁵ cm⁻¹ for well-isolated lines. The combined line list of 8522 water lines is assigned to 9186 transitions of the nine stable water isotopologues (H₂^XO, HD^XO, and D₂^XO with X = 16, 17, and 18). Regarding the HD¹⁶O isotopologue, a total of 2443 transitions are presently assigned while about 530 absorption transitions were available prior to our SOLEIL recordings. The comparison with the HITRAN list of HD¹⁶O transitions is discussed in detail. The obtained set of accurate HD¹⁶O transition frequencies is merged with literature sources to generate a set of 1121 accurate empirical rotation–vibration energies for the first five vibrational states (000), (010), (100), (020), and (001). The comparison to the previous dataset from an IUPAC task group illustrates a gain in the average energy accuracy by more than one order of magnitude. Based on these levels, a recommended list of transitions between the first five vibrational states is proposed for HD¹⁶O in the 0–4650 cm⁻¹ frequency range. Full article

The smoothing of linear toolpaths plays is critical in improving machining quality and efficiency in five-axis CNC machining. Existing corner-smoothing methods often overlook the impact of spline curvature fluctuations, which may lead to acceleration variations, hindering surface quality improvements. The paper presents a five-axis toolpath corner-smoothing method based on the space of master–slave movement (SMM), aiming to minimize curvature fluctuations in five-axis machining and improve surface quality. The concept of movement space in master–slave cooperative motion is introduced, where the tool tip position and tool orientation are decoupled into a main motion trajectory and two master–slave movement space trajectories. By deriving the curvature monotony conditions of a dual Bézier spline, a G2-continuous tool tip corner-smoothing curve with minimal curvature fluctuations is constructed in real-time. Subsequently, using the SMM and the asymmetric dual Bézier spline, a high-order continuous synchronization relationship between the tool tip position and tool orientation is established. Simulation tests and machining experiments show that with our smoothing algorithm, maximum acceleration values for each axis were reduced by 21.05%, while jerk was lowered by 22.31%. These results indicate that trajectory smoothing significantly reduces mechanical vibrations and improves surface quality. Full article

In order to ensure the safe operation of adjacent buried pipelines under blast vibration, it is of great practical engineering significance to accurately predict the peak vibration velocity ofburied pipelines under blasting loads. Relying on the test results of the buried steel pipe blast model test, a sensitivity analysis of relevant influencing factors was carried out by using the gray correlation analysis method. A least squares support vector machine (LS-SVM) model was established to predict the peak vibration velocity of the pipeline and determine the best parameter combination in the LS-SVM model through a local particle swarm optimization (PSO), and the results of the PSO-LSSVM model were predicted. These were compared with BP neural network model and Sa’s empirical formula. The results show that the fitting correlation coefficient (R2), root mean square error (RMSE), average relative error (MRE), and Nash coefficient (NSE) of the PSO-LSSVM model for the prediction of pipeline peak vibration velocity are 91.51%, 2.95%, 8.69%, and 99.03%, showing that the PSO-LSSVM model has a higher prediction accuracy and better generalization ability, which provides a new idea for the vibration velocity prediction of buried pipelines under complex blasting conditions. Full article

Effectively controlling the adsorption and desorption of coal and mine gas is crucial to preventing harm to the environment. Therefore, this paper investigated the adsorption of coal and methane molecules from the perspective of microscopic energy through Gaussian simulation. Gaussian 09W and GaussView 5.0 software were used to construct and optimize the molecular model of four different metamorphic coals, namely lignite, sub-bituminous coal, bituminous coal, and anthracite, and their adsorption structure with methane as well as the energy, bond length, vibration frequency, infrared spectrum, and other data on the optimal structure were obtained. The binding energy of coal molecules and methane from large to small was as follows: sub-bituminous coal (7.3696 KJ/mol), lignite (6.6149 KJ/mol), bituminous coal (5.2170 KJ/mol), and anthracite (4.9510 KJ/mol). The equilibrium distance was negatively correlated with the binding energy, and the molecular structure and position of coal largely determined the binding energy. Additionally, adsorption was more likely to occur between methane molecules and hydroxyl groups. Many new vibration modes were observed during the adsorption of coal and methane molecules. This paper is of practical significance, as studying the adsorption of coal and mine gas can prevent and control mine gas outbursts and ensure safe production. Full article

Chemical warfare agents (CWAs) are known as poor man’s bombs because of their small lethal dose, cheapness, and ease of production. Therefore, the highly sensitive and rapid detection of CWAs at room temperature (RT = 25 °C) is essential. In this paper, we have developed a resistive semiconductor sensor for the highly sensitive detection of CWAs at RT. The gas-sensing material is SnS₂/rGO nanosheets (NSs) prepared by hydrothermal synthesis. The lower detection limits of the SnS₂/rGO NSs-based gas sensor were 0.05 mg/m³ and 0.1 mg/m³ for the typical chemical weapons sarin (GB) and sulfur mustard (HD), respectively. The responsivity can reach −3.54% and −10.2% in 95 s for 1.0 mg/m³ GB, and in 47 s for 1.0 mg/m³ HD. They are 1.17 and 2.71 times higher than the previously reported Nb-MoS₂ NSs-based gas sensors, respectively. In addition, it has better repeatability (RSD = 6.77%) and stability for up to 10 weeks (RSD = 20.99%). Furthermore, to simplify the work of later researchers based on the detection of CWAs by two-dimensional transition metal sulfur compounds (2D-TMDCs), we carried out calculations of the SnS₂ NSs-based and SnS₂/rGO NSs-based gas sensor-adsorbing CWAs. Detailed comparisons are made in conjunction with experimental results. For different materials, it was found that the SnS₂/rGO NSs-based gas sensor performed better in all aspects of adsorbing CWAs in the experimental results. Adsorbed CWAs at a distance smaller than that of the SnS₂ NSs-based gas sensor in the theoretical calculations, as well as its adsorption energy and transferred charge, were larger than those of the SnS₂ NSs-based gas sensor. For different CWAs, the experimental results show that the sensitivity of the SnS₂/rGO NSs-based gas sensor for the adsorption of GB is higher than that of HD, and accordingly, the theoretical calculations show that the adsorption distance of the SnS₂/rGO NSs-based gas sensor for the adsorption of GB is smaller than that of HD, and the adsorption energy and the amount of transferred charge are larger than that of HD. This regularity conclusion proves the feasibility of adsorption of CWAs by gas sensors based on SnS₂ NSs, as well as the feasibility and reliability of theoretical prediction experiments. This work lays a good theoretical foundation for subsequent rapid screenings of gas sensors with gas-sensitive materials for detecting CWAs. Full article

Background/Objectives: This study investigated the impacts of a 12-week training program on kinetic parameters during landings in non-professional recreational athletes. Methods: Fifty-seven non-elite recreational athletes performed three unilateral-landing trials from a 30 cm high structure on a force platform. The following outcome measures were analyzed: the ground reaction forces at initial ground contact (PF1) and the highest value (PF2), impulse (change in the moment of force during landing), stabilization time, and ankle and knee accelerations. The ground reaction forces, momentum, and accelerations were evaluated in the vertical, medio-lateral, and anteroposterior axes. Participants were randomly assigned to two groups. The experimental group (EG) underwent a 12-week intervention, three times per week, consisting of various exercises, such as strength, eccentric, proprioceptive, whole-body vibration (WBV), and neuromuscular exercises. After 12 weeks, the same outcome measures were analyzed. Results: The effects of the training program on vertical ground reaction forces were not clear (0.3% and 0.9%, respectively). Medio-lateral (64.8%, d = 0.51) and anteroposterior (43.9%, d = 1.34) forces were probably reduced due to the training program. The stabilization time was also reduced in the experimental group (44.2%). The training program most likely decreased the vertical impulse (47.3%, d = −1.56), whereas the total ankle acceleration increased (18.4%, d = 0.79). Conclusions: The findings reveal significant improvements in neuromuscular control and stability during landings, specifically demonstrating reduced medio-lateral forces, vertical momentum, and acceleration during monopodal landings. This study focuses on the importance of proper landing techniques in minimizing the risk of knee injuries, emphasizing the falling strategy’s role in injury prevention. Full article

During roadway excavation, the presence of roof deterioration zones, such as layered spaces and weak interlayers, significantly affects the stability of the surrounding rock. To achieve timely and effective support for roadways, it is essential to utilize drilling measurement signals obtained during the construction of anchorage holes for the identification and prediction of these deterioration zones. This study systematically investigates the response characteristics of thrust, torque, and Y-direction vibration signals to different combinations of rock layers through theoretical analysis, laboratory experiments, ABAQUS dynamic numerical simulations, and field measurements. The results indicate that these drilling parameters effectively characterize variations in rock structure and strength, with distinct signal features observed particularly in roof deterioration zones. Based on these findings, this paper proposes a deep learning algorithm that employs Long Short-Term Memory (LSTM) recurrent neural networks for classification prediction, along with a random forest algorithm for regression prediction, aimed at the intelligent identification and prediction of roof deterioration zones. The algorithm demonstrates outstanding performance in both laboratory experiments and field tests, achieving a 100% recognition rate for layered spaces and a 96.6% accuracy for identifying deterioration zones, with high accuracy at lower values of mechanical specific energy (MSE). The proposed method provides significant insights for real-time monitoring and control of roof deterioration zones, enhancing the safety and stability of roadway excavations, and serves as a valuable reference for future research and practical applications. Full article

This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, which can compromise system stability and production efficiency. To enhance diagnostic accuracy, the research employs wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) to extract multi-scale features from vibration signals. These features are subsequently fused, and GSA is used to optimize the high-dimensional fused features, eliminating redundant data and mitigating overfitting. The optimized features are then input into a support vector machine (SVM) for fault classification, with K-fold cross-validation used to assess the model’s generalization capabilities. Experimental results demonstrate that the proposed diagnosis method, which integrates multi-sensor data fusion with GSA optimization, significantly improves fault diagnosis accuracy compared to methods using single-sensor signals or unoptimized features. This improvement is particularly notable in multi-class fault scenarios. Additionally, GSA’s global search capability effectively addresses overfitting issues caused by high-dimensional data, resulting in a diagnostic model with greater reliability and accuracy across various fault conditions. Full article

Efficient and accurate fault diagnosis of rotating machinery is extremely important. Fault diagnosis methods using vibration signals based on convolutional neural networks (CNNs) have become increasingly mature. They often struggle with capturing the temporal dynamics of vibration signals. To overcome this, the application of Transformer-based Vision Transformer (ViT) methods to fault diagnosis is gaining attraction. Nonetheless, these methods typically require extensive preprocessing, which increases computational complexity, potentially reducing the efficiency of the diagnosis process. Addressing this gap, this paper presents the Time Series Vision Transformer (TSViT), tailored for effective fault diagnosis. The TSViT incorporates a convolutional layer to extract local features from vibration signals alongside a transformer encoder to discern long-term temporal patterns. A thorough experimental comparison of three diverse datasets demonstrates the TSViT’s effectiveness and adaptability. Moreover, the paper delves into the influence of hyperparameter tuning on the model’s performance, computational demand, and parameter count. Remarkably, the TSViT achieves an unprecedented 100% average accuracy on two of the test sets and 99.99% on the other, showcasing its exceptional fault diagnosis capabilities for rotating machinery. The implementation of this model will bring significant economic benefits. Full article

This article discusses the issue of the joint use of neural network algorithms for data processing and deterministic mathematical models. The use of a new approach is proposed, to determine the discrepancy between data from a vibration monitoring system of the cutting process and the calculated data obtained by modeling mathematical models of the digital twin system of the cutting process. This approach is justified by the fact that some coordinates for the state of the cutting process cannot be measured, and the vibration signals measured by the vibration monitoring system (the vibration acceleration of the tip of the cutting tool) are subject to external disturbing influences. Both the experimental method and the Matlab 2022b simulation method were used as research methods. The experimental research method is based on the widespread use of modern analog vibration transducers, the signals from which undergo the process of digitalization and further processing in order to identify arrays of additional information required for virtual digital twin models. The results obtained allow us to formulate a new conceptual approach to the construction of systems for determining the degree of cutting tool wear, based on the joint use of computational virtual models of the digital twin system and data obtained from the vibration monitoring system of the cutting process. Full article

In this paper, we investigate the inverse of the set of unknown functions $(v,g)$ of the Burgers equation in the framework of optimal theory. Firstly, we prove the existence of functional minimizers in the optimal control problem and derive the necessary conditions for the optimal solution. Subsequently, the global uniqueness of the optimal solution and its stability are explored. After completing the ill-posed analysis of the Burgers equation, we can apply it to the problem of sonic vibration velocity in water. The desired result is obtained by inverse-performing an unknown initial state with known terminal vibration velocity. This is important for solving practical problems. Full article

Seismic data acquired in the presence of mechanical vibrations or power facilities may be contaminated by strong interferences, significantly decreasing the data signal-to-noise ratio (S/N). Conventional methods, such as the notch filter and time-frequency transform method, are usually inadequate for suppressing non-stationary interference noises, and may distort effective signals if overprocessing. In this study, we propose a method for eliminating mechanical vibration interferences in seismic data. In our method, we extended the variational mode extraction (VME) technique to a multivariate form, called multivariate variational mode extraction (MVME), for synchronous analysis of multitrace seismic data. The interference frequencies are determined via synchrosqueezing-based time-frequency analysis of process recordings; their corresponding modes are extracted and removed from seismic data using MVME with optimal balancing factors. We used synthetic data to investigate the effectiveness of the method and the influence of tuning parameters on processing results, and then applied the method to field datasets. The results have demonstrated that, compared with the conventional methods, the proposed method could effectively suppress the mechanical vibration interferences, improve the S/Ns and enhance polarization analysis of seismic signals. Full article

Unlike traditional free-piston Stirling heat engines or heat pumps, the free piston Stirling air conditioning (FPSAC) is specifically designed for electric vehicle air conditioning under ambient room temperature conditions. In the FPSAC system, the displacer and the power piston are coupled through gas forces, emphasizing the importance of investing the thermodynamic–kinetic coupling characteristics. This study analyzed the damping terms within the dynamic equations of the FPSAC model and solved these equations to reveal system dynamics. By linearizing the working chamber’s pressure, the study examined the machine’s dynamic behavior, presenting solutions for amplitude and phase angle. Derived expressions for the displacement and acceleration of both the power piston and the displacer further support this analysis. The research evaluates the influence of driving force on amplitude and phase angle, alongside the impact of damping coefficients, thereby isolating thermodynamic–dynamic coupling characteristics. Control equations integrating dynamics and thermodynamics were developed, and a comprehensive system model was constructed using MATLAB(2020a)/Simulink to simulate acceleration and displacement variation in the pistons. Key findings include: (1) a positive correlation between driving force and displacer, where increased force leads to higher amplitudes; (2) a frequency of 65 Hz reveals a singularity occurs in displacer amplitude, resulting in system instability; (3) phase angle between pistons reduces to below 10° when the driving force exceeds 150 N; and (4) the power piston’s amplitude decreases with an increase in damping C_1, while changes in damping C₂ primarily affect the displacer’s singularity position around 65 Hz, with higher C₂ values shifting the singularity to lower frequencies. Full article

Water-lubricated rubber bearings are a critical component of the propulsion systems in underwater vehicles. Particularly under conditions of low speed and high load, friction-induced vibration and wear often occur. Surface texturing technology has been proven to improve lubrication performance and reduce friction and wear; however, research on how different texture parameters affect friction-induced vibration and wear mechanisms remains insufficient. In this study, various texture patterns with different area ratios and aspect ratios were designed on the surface of water-lubricated rubber bearings. By combining these designs with an in situ observation system based on computer vision technology, the effects of texture parameters on bearing friction, vibration, and wear were thoroughly investigated. The experimental results show that surface textures play a critical role in improving hydrodynamic effects and stabilizing the lubrication film at the friction interface. Specifically, textures with a high area ratio (15%) and aspect ratio (3:1) exhibited the best vibration suppression effect, primarily due to the reduction in actual contact area. However, excessively high area ratios may lead to increased surface wear. This study concludes that a reasonable selection of texture area and aspect ratios can significantly reduce frictional force fluctuations and vibration amplitude, minimize surface wear, and extend bearing life. Full article

This paper presents a comprehensive survey of state-of-the-art UAV–based antennas and propagation measurements. Unmanned aerial vehicles (UAVs) have emerged as powerful tools for in situ electromagnetic field assessments due to their flexibility, cost-effectiveness, and ability to operate in challenging environments. This paper highlights various UAV applications, from testing large–scale antenna arrays, such as those used in the square kilometer array (SKA), to evaluating channel models for 5G/6G networks. Additionally, the review discusses technical challenges, such as positioning accuracy and antenna alignment, and it provides insights into the latest advancements in portable measurement systems and antenna designs tailored for UAV use. During the UAV–based antenna measurements, key contributors to the relatively small inaccuracies of around 0.5 to 1 dB are identified. In addition to factors such as GPS positioning errors and UAV vibrations, ground reflections can significantly contribute to inaccuracies, leading to variations in the measured radiation patterns of the antenna. By minimizing ground reflections during UAV–based antenna measurements, errors in key measured antenna parameters, such as HPBW, realized gain, and the front-to-back ratio, can be effectively mitigated. To understand the source of propagation losses in a UAV to ground link, simulations were conducted in CST. These simulations identified scattering effects caused by surrounding buildings. Additionally, by simulating a UAV with a horn antenna, potential sources of electromagnetic coupling between the antenna and the UAV body were detected. The survey concludes by identifying key areas for future research and emphasizing the potential of UAVs to revolutionize antenna and propagation measurement practices to avoid the inaccuracies of the antenna parameters measured by the UAV. Full article