Search Results (21)

Background and Objectives: Anterior cruciate ligament (ACL) injury rates remain high and have a significant impact on female football players. This study aims to evaluate knee kinematics and lower limb muscle activity in players at risk of ACL injury compared to healthy players through three side-cutting tests. It also investigates how the amplitude of a change in direction influences stabilization parameters. Materials and Methods: A cross-sectional case–control study was conducted with 16 second division female futsal players (23.93 ± 5.16 years), divided into injured (n = 8) and healthy groups (n = 8). Injured players had a history of non-contact knee injury involving valgus collapse, without undergoing surgical intervention. Three change of direction tests, namely the Change of Direction and Acceleration Test (CODAT), Go Back (GOB) test, and Turn (TURN) test, were used for evaluation. The peak and range of knee joint angles and angular velocities across three planes, along with the average rectified and peak envelope EMG signals of the Biceps Femoris (BF), Semitendinosus (ST), Vastus Medialis (VM), and Lateral Gastrocnemius (LG), were recorded during the preparation and load phases. Group differences were analyzed using two-factor mixed-model ANOVA with pairwise comparisons. Statistical significance was set at p < 0.05. Results: Injured players demonstrated lower external tibial rotation angular velocity and a greater range of motion in tibial external rotation compared to healthy players. Additionally, the injured group showed significantly higher average rectified muscle activity in VM and LG both increased by 4% during the load phase. The CODAT and TURN tests elicited higher BF and VM muscle activity, compared to the GOB test. The TURN test also showed greater extension angular velocity in the sagittal plane. Conclusions: The results revealed differences in knee kinematics and muscle activity between players at risk of ACL injury and healthy players, influenced by the amplitude of directional changes. Players altered transverse plane mechanics and increased VM and LG activation during LOAD may reflect a dysfunctional motor pattern, while the greater sagittal plane angular velocity and VM and BF activation from the CODAT and the TURN test highlight their higher potential to replicate ACL injury mechanisms compared to the GOB test. Full article

Coal mining disrupts soil structure and causes water loss, thereby affecting the ecological environment of mining areas. Rapid, accurate, and non-destructive detection of surface soil moisture is crucial for advancing ecological restoration in these regions. This study focuses on the mined and unmined areas of the Yushuquan coal mine, located on the southern slope of the Tianshan Mountains in Xinjiang, China. The soil volumetric water content (SVWC) was measured using time-domain reflectometry (TDR), while the shallow subsurface soil was investigated using ground-penetrating radar (GPR). Various features were extracted from GPR signals in both the time- and frequency-domains, and their relationships with SVWC were analyzed. Multiple features were selected and optimized to determine the optimal feature combination for building a multi-feature backpropagation neural network model for soil volumetric water content prediction (Muti-BP-SVWC). The performance of this model was compared with two single-feature-based methods for SVWC prediction: the average envelope amplitude (AEA) method and the frequency shift method. The application results of the Muti-BP-SVWC model in different regions demonstrated significant improvements in accuracy and stability compared to the AEA method and the frequency shift method. In the mined area validation set, the model achieved an determination coefficient (R²) of 0.77 and the root mean square error (RMSE) of 0.0091 cm³/cm³, while in the unmined area validation set, the R² of 0.84 and an RMSE of 0.0059 cm³/cm³. These results indicate that incorporating multiple features into the BP neural network can better capture the complex relationship between GPR signals and SVWC. This approach effectively inverts the shallow subsurface soil moisture in mining areas and provides valuable guidance for ecological restoration in these regions. Full article

This study investigates the hypothesis that meteorological anomalies may precede earthquake events. Long-term time series of observations for air temperature, atmospheric pressure and precipitation at a meteorological station in Kamchatka are considered. Time series are subjected to Huang decomposition into sequences of levels of empirical oscillation modes (intrinsic mode functions—IMFs), forming a set of orthogonal components with decreasing average frequency. For each IMF level, the instantaneous amplitudes of envelopes are calculated using the Hilbert transform. A comparison with the earthquake sequence is made using a parametric model of the intensity of two interacting point processes, which allows one to quantitatively estimate the “measure of the lead” of the time instants of the compared sequences. For each IMF level, the number of time moments of the largest local maxima of instantaneous amplitudes which is equal to the number of earthquakes is selected. As a result of the analysis, it turned out that for the sixth IMF level (periods of 8–16 days), the “lead measure” of the instantaneous amplitude maxima of meteorological parameters in comparison with earthquake time moments significantly exceeds the inverse lead, which confirms the existence of prognostic changes in meteorological parameters in the problem of “atmosphere–lithosphere” interaction. This study reveals that certain meteorological anomalies can be a precursor for seismic activity. Full article

Monitoring the dynamic behaviors of self-aligning roller bearings (SABs) is vital to guarantee the stability of various mechanical systems. This study presents a novel self-powered, intelligent, and self-aligning roller bearing (I-SAB) with which to monitor rotational speeds and bias angles; it also has an application in fault diagnosis. The designed I-SAB is compactly embedded with a novel sweep-type triboelectric nanogenerator (TENG). The TENG is realized within the proposed I-SAB using a comb–finger electrode pair and a flannelette triboelectric layer. A floating, sweeping, and freestanding mode is utilized, which can prevent collisions and considerably enhance the operational life of the embedded TENG. Experiments are subsequently conducted to optimize the output performance and sensing sensitivity of the proposed I-SAB. The results of a speed-sensing experiment show that the characteristic frequencies of triboelectric current and voltage signals are both perfectly proportional to the rotational speed, indicating that the designed I-SAB has the self-sensing capability for rotational speed. Additionally, as both the bias angle and rotational speed of the SAB increase, the envelope amplitudes of the triboelectric voltage signals generated by the I-SAB rise at a rate of 0.0057 V·deg⁻¹·rpm⁻¹. To further demonstrate the effectiveness of the triboelectric signals emitted from the designed I-SAB in terms of self-powered fault diagnosis, a Multi-Scale Discrimination Network (MSDN), based on the ResNet18 architecture, is proposed in order to classify the various fault conditions of the SAB. Using the triboelectric voltage and current signals emitted from the designed I-SAB as inputs, the proposed MSDN model yields excellent average diagnosis accuracies of 99.8% and 99.1%, respectively, indicating its potential for self-powered fault diagnosis. Full article

Stored products, such as grains and processed foods, are susceptible to infestation by various insects. The early detection of insects in the supply chain is crucial, as introducing invasive pests to new environments may cause disproportionate harm. The STAR Center at Stevens Institute of Technology developed the Acoustic Stored Product Insect Detection System (A-SPIDS) to detect pests in stored products. The system, which comprises a sound-insulated container for product samples with a built-in internal array of piezoelectric sensors and additional electret microphones to record outside noise, was used to conduct numerous measurements of the vibroacoustic signatures of various insects, including the Callosobruchus maculatus, Tribolium confusum, and Tenebrio molitor, in different materials. A normalization method was implemented using the ambient noise of the sensors as a reference, to accommodate for the proprietary, non-calibrated sensors and allowing to set relative detection thresholds for unknown sensitivities. The normalized envelope of the filtered signals was used to characterize and compare the insect signals by estimating the Normalized Signal Pulse Amplitude (NSPA) and the Normalized Spectral Energy Level (NSEL). These parameters characterize the insect detection Signal Noise Ratio (SNR) for pulse-based detection (NSPA) and averaged energy-based detection (NSEL). These metrics provided an initial step towards the design of a reliable detection algorithm. In the conducted tests NSPA was significantly larger than NSEL. The NSPA reached 70 $\mathrm{d}$$\mathrm{B}$ for T. molitor in corn flakes. The insect signals were lower in flour where the averaged NSPA and NSEL values were around 40 dB and 11 $\mathrm{d}$$\mathrm{B}$ to 16 $\mathrm{d}$$\mathrm{B}$, respectively. Full article

Blowoff limits are essential in establishing the combustor operating envelope. Hence, there is a great demand for practical aero-engines to extend the blowoff limits further. In this work, the behavior of non-premixed swirling flames under fuel flow rate oscillations was investigated experimentally close to its blowoff limits. The methane flame was stabilized on the axisymmetric bluff body and confined in a square quartz enclosure. External acoustic forcing at 400 Hz was applied to the fuel flow to induce a fuel mass flow rate fluctuation (FMFRF) with varying amplitudes. A high-speed burst-mode laser and cameras ran at 20 kHz for OH*-chemiluminescence (CL), CH₂O-, and SO₂-PLIF measurements, offering the visualization of the two-dimensional flame structure and heat release distribution, temporally and spatially. The results show that the effect of FMFRF is predominantly along the central axis without altering the time-averaged flame structure and blowoff transient. However, the blowoff limits are extended due to the enhanced temperature and longer residence time induced by FMFRF. This work allows us to explore the mechanism of flame instability further. Full article

To increase the detection accuracy of soil structure and moisture content in reconstituted soils under complex conditions, this study utilizes a 400 MHz ground-penetrating radar (GPR) to examine a study area consisting of loess, sandy loam, red clay, and mixed soil. The research involves analyzing the single-channel waveforms and two-dimensional images of GPR, preprocessing the data, obtaining envelope information via amplitude envelope detection, and performing a Hilbert transformation. This study employs a least squares fitting approach to the instantaneous phase envelope to ascertain the thickness of various soil layers. By utilizing the average envelope amplitude (AEA) method, a correlation between the radar’s early signal amplitude envelope and the soil’s shallow dielectric constant is established to invert the moisture content of the soil. The analysis integrates soil structure and moisture distribution data to investigate soil structure characteristics and moisture content performance under diverse soil properties and depths. The findings indicate that the envelope detection method effectively identifies stratification boundaries across different soil types; the AEA method is particularly efficacious in inverting the moisture content of reconstituted soils up to 3 m deep, with an average relative error ranging from 2.81% to 7.41%. Notably, moisture content variations in stratified reconstituted soils are more pronounced than those in mixed soil areas, displaying a marked stepwise increase with depth. The moisture content trends in the vertical direction of the same soil profile are generally consistent. This research offers a novel approach to studying reconstituted soils under complex conditions, confirming the viability of the envelope detection and AEA methods for intricate soil investigations and broadening the application spectrum of GPR in soil studies. Full article

Rotating machinery fault diagnosis is of key significance for ensuring safe and efficient operation of various industrial equipment. However, under nonstationary operating conditions, the fault–induced characteristic frequencies are often time–varying. Conventional Fourier spectrum analysis is not suitable for revealing time–varying details, and nonstationary fault feature extraction methods are still in desperate need. Order spectrum can reveal the rotational–speed–related time–varying frequency components as spectral peaks in order domain, thus facilitating fault feature extraction under time–varying speed conditions. However, the speed–unrelated frequency components are still nonstationary after angular–domain resampling, thus causing wide–band features and interferences in the order spectrum. To overcome such a drawback, this work proposes a rotating machinery fault diagnosis method based on adaptive separation of time–varying components and order feature extraction. Firstly, the rotational speed is estimated by the multi–order probabilistic approach (MOPA), thus eliminating the inconvenience of installing measurement equipment. Secondly, adaptive separation of the time–varying frequency component is achieved through time–varying filtering and surrogate test. It effectively eliminates interference from irrelevant components and noise. Finally, a high–resolution order spectrum is constructed based on the average amplitude envelope of each mono–component. It does not involve Fourier transform or angular–domain resampling, thus avoiding spectral leakage and resampling errors. By identifying the fault–related spectral peaks in the constructed order spectrum, accurate fault diagnosis can be achieved. The Rényi entropy values of the proposed order spectrum are significantly lower than those of the traditional order spectrum. This result verifies the effective energy concentration and high resolution of the proposed order spectrum. The results of both numerical simulation and lab experiments confirm the effectiveness of the proposed method in accurately presenting the time–varying frequency components for rotating machinery diagnosing faults. Full article

To quantitatively identify internal wire breakage damage in mining wire ropes, a wire rope internal wire breakage signal identification method is proposed. First, the whale optimization algorithm is used to find the optimal value of the variational mode decomposition parameter [$\mathrm{K},\mathsf{\alpha }]$ to obtain the optimal combination of the parameters, which reduces the signal noise with a signal-to-noise ratio of 29.29 dB. Second, the minimum envelope entropy of the noise reduction signal is extracted and combined with the time-domain features (maximum and minimum) and frequency-domain features (frequency–amplitude average, average frequency, average power) to form a fusion feature set. Finally, we use a particle swarm optimization–least squares support vector machine model to identify the internal wire breakage of wire ropes. The experimental results show that the method can effectively identify the internal wire rope breakage damage, and the average recognition rate is as high as 99.32%, so the algorithm can greatly reduce the system noise and effectively identify the internal damage signal of the wire rope, which is superior to a certain extent. Full article

In this paper, we review our current understanding of the outer envelope structures of massive stars based on three-dimensional (3D) radiation hydrodynamic simulations. We briefly summarize the fundamental issues in constructing hydrostatic one-dimensional (1D) stellar evolution models when stellar luminosity approaches the Eddington value. Radiation hydrodynamic simulations in 3D covering the mass range from $13M_{\odot }$ to $80M_{\odot }$ always find a dynamic envelope structure with the time-averaged radial profiles matching 1D models with an adjusted mixing-length parameter when convection is subsonic. Supersonic turbulence and episodic mass loss are generally found in 3D models when stellar luminosity is super-Eddington locally due to the opacity peaks and convection being inefficient. Turbulent pressure plays an important role in supporting the outer envelope, which makes the photosphere more extended than predictions from 1D models. Massive star lightcurves are always found to vary with a characteristic timescale consistent with the thermal time scale at the location of the iron opacity peak. The amplitude of the variability as well as the power spectrum can explain the commonly observed stochastic low-frequency variability of mass stars observed by TESS over a wide range of parameters in an HR diagram. The 3D simulations can also explain the ubiquitous macro-turbulence that is needed for spectroscopic fitting in massive stars. Implications of 3D simulations for improving 1D stellar evolution models are also discussed. Full article

Ground-penetrating radar (GPR) is an important tool for measuring soil water content (SWC) at the field scale. The amplitude analysis of the early time signal (ETS) of GPR may provide a rapid way to estimate SWC. By assuming a homogeneous medium, various studies have been conducted on the relationship between the amplitude of ETS and the topsoil layer’s electromagnetic parameters (dielectric permittivity and conductivity) through numerical simulations, laboratory experiments, and field experiments. Soil is a typical inhomogeneous medium, and soil cultivation is a factor affecting its heterogeneity. In this context, we discuss the ability of the amplitude of ETS to estimate the water content of heterogeneous soil. First, we establish a multi-scale stochastic medium model with the inhomogeneous distribution of dielectric permittivity and conductivity and simulate the GPR response by the finite-difference time-domain (FDTD) method to observe the influence of medium heterogeneity on the GPR response. The heterogeneity of the soil models is evaluated by a geostatistical analysis described by two parameters, correlation length and variability. Then, we analyze the relationship between variability and the average envelope amplitude (AEA) of ETS. A strong soil heterogeneity increases the error of the AEA method in estimating SWC. Finally, the AEA method is used to estimate the SWC of two adjacent fields with different heterogeneities, which were caused by different cultivation methods. The results of the numerical simulation and field experiment indicate that the soil heterogeneity can have an impact on the estimation of SWC using EST, with an error lower than 3% within a depth range of 1/2 λ to λ (wavelength). This suggests that the EST of GPR can be applied to soil layers with relatively large lateral changes in water content. Full article

Predictive maintenance is increasingly popular in many branches, as well as in the mining industry; however, there is a lack of spectacular examples of its practice efficiency. Close collaboration between Omya Group and Wroclaw University of Science and Technology allowed investigation of the failure of the inertial vibrator’s bearing. The signals of vibration are captured from the sieving screen just before bearing failure and right after repair, when it was visually inspected after replacement. The additional complication was introduced by the loss of stable attachment of the vibrator’s shield, which produced great periodical excitation in each place of measurement on the machine. Such anomalies in the signals, in addition to falling pieces of material, made impossible the diagnostics by standard methods. However, the implementation of advanced signal processing techniques such as time–frequency diagrams, envelope spectrum, cyclic spectral coherence, orbits analysis, and phase space plots allowed to undermine defects (pitting on the inner ring). After repair, the amplitudes of vibration from the damaged bearing side were reduced by five times, while sound pressure was only two times lower. The quantitative parameters of vibrations showed significant changes: time series RMS (−68%) median energy of spectrograms (89%), frequencies ratio of cyclic spectral coherence (−85%), and average amplitude of harmonics in envelope spectrum (−80%). The orbits demonstrated changes in inclination angle (16%) and sizes (−48, … −96%), as well as phase space plots sizes (−28, … −67%). Directions of further research are considered. Full article

For water dynamics investigation in unsaturated (vadose) zones, ground penetrating radar is a popular hydro-geophysical method because it is non-invasive for soil, has high resolution and the results have a direct link with water content. Soil water content and soil hydraulic properties are two key factors for describing the water dynamics in vadose zones. There has been tremendous progress in soil water content and soil hydraulic properties estimation with ground penetrating radar. The purpose of this paper is to provide an overview of the application of ground penetrating radar for soil water dynamics studies. This paper first summarizes various methods for the determination of soil water content. including traditional methods in the surveys of surface ground penetrating radar, borehole ground penetrating radar, and off-ground ground penetrating radar, as well as relatively new methods, such as full waveform inversion, the average envelope amplitude method, and the frequency shift method. This paper further provides a review for estimating soil hydraulic properties with GPR according to the types of ground penetrating radar data. We hope that this review can provide a reference for the application of ground penetrating radar in soil water dynamics studies in the future. Full article

The lack of long-term observational data on secondary PM_2.5 (SPM) has limited our comprehensive understanding of atmospheric environment change. This study develops an SPM estimation method, named Single-Tracer Approximate Envelope Algorithm (STAEA), to assess the long-term changes of SPM under different PM_2.5 levels and in all seasons in Wuhan, Central China, over the period of anthropogenic pollutant mitigation in 2015–2020. The results show that: (1) the average proportions of SPM in ambient PM_2.5 is 59.61% in a clean air environment, rising significantly to 71.60%, 73.73%, and 75.55%, respectively, in light, moderate, and heavy PM_2.5 pollution, indicating the dominant role of SPM in air quality deterioration; (2) there are increasing trends of interannual changes of SPM at the light and moderate pollution levels of 1.95 and 3.11 μg·m⁻³·a⁻¹ with extending SPM proportions in PM_2.5 pollution, raising a challenge for further improvement in ambient air quality with mitigating light and moderate PM_2.5 pollution; (3) the high SPM contributions ranging from 55.63% to 68.65% on a seasonal average and the large amplitude of seasonal SPM changes could dominate the seasonality of air quality; (4) the wintertime SPM contribution present a consistent increasing trend compared with the declining trends in spring, summer, and autumn, suggesting underlying mechanisms of SPM change for further deciphering the evolution of the atmospheric environment. Our results highlight the effects of air pollutant mitigation on long-term variations in SPM and its contributions with implications for atmospheric environment change. Full article

Besides phase variability, amplitude variability is one of the two manifestations of the spatially varying ground motion (SVGM) in the frequency domain. Neglecting the amplitude variability of the earthquake spectra can result in an underestimation of the structural responses. Few existing amplitude variability models can be used for estimating spectra at a distance from a reference location. In this paper, a new amplitude variability model describing the evolution of the energy spectra is developed based on records of five earthquake events acquired from the SMART 1 array. The similarity between the spectra of two locations is used as a metric for measuring the spectrum changes. The energy spectra at a distance from a reference location are found to be composed of two parts, including the averaged spectra and the random variation part. In the new model, the former is estimated by the moving average of the reference spectrum, while the envelope of the latter is approximated by the wavelet approximation of the reference spectrum’s Fourier amplitude spectrum. The parameters of five models for each event and a general model for all five events are identified. Monte Carlo simulations are used for testing the models. The results validate the new model in terms of capturing the similarity changes of actual earthquakes. Full article