Search Results (279)

Recently, we proposed a new method, based on protein profiles derived from physicochemical dynamic time warping (PCDTW), to functionally/structurally classify coronavirus spike protein receptor binding domains (RBD). Our method, as used herein, uses waveforms derived from two physicochemical properties of amino acids (molecular weight and hydrophobicity (MWHP)) and is designed to reach into the twilight zone of homology, and therefore, has the potential to reveal structural/functional relationships and potentially homologous relationships over greater evolutionary time spans than standard primary sequence alignment-based techniques. One potential application of our method is inferring deep evolutionary relationships such as those between the RBD of the spike protein of betacoronaviruses and functionally similar proteins found in other families of viruses, a task that is extremely difficult, if not impossible, using standard multiple alignment-based techniques. Here, we applied PCDTW to compare members of four divergent families of viruses to betacoronaviruses in terms of MWHP physicochemical similarity of their RBDs. We hypothesized that some members of the families Arteriviridae, Astroviridae, Reoviridae (both from the genera rotavirus and orthoreovirus considered separately), and Toroviridae would show greater physicochemical similarity to betacoronaviruses in protein regions similar to the RBD of the betacoronavirus spike protein than they do to other members of their respective taxonomic groups. This was confirmed to varying degrees in each of our analyses. Three arteriviruses (the glycoprotein-2 sequences) clustered more closely with ACE2-binding betacoronaviruses than to other arteriviruses, and a clade of 33 toroviruses was found embedded within a clade of non-ACE2-binding betacoronaviruses, indicating potentially shared structure/function of RBDs between betacoronaviruses and members of other virus clades. Full article

Linear frequency modulation (LFM) waveforms have been widely adopted due to their excellent performance characteristics, such as good Doppler tolerance and ease of physical implementation. However, LFM waveforms suffer from high autocorrelation sidelobes (ACSLs) and limited design flexibility. Phase-coded frequency modulation (PCFM) waveforms can be used to design waveforms similar to LFM, offering greater design flexibility to optimize ACSLs. However, it has been found that the initial PCFM waveform experiences spectral expansion during the ACSL optimization process, which reduces its similarity to LFM. Therefore, this article jointly optimizes the ACSLs and spectrum of the initial PCFM waveform, establishes an optimized mathematical model, and then solves it using the heavy-ball gradient descent algorithm. Numerical experiments indicate that the proposed method effectively addresses the problem of waveform similarity degradation caused by spectral expansion while reducing waveform ACSLs. At the same time, a balance between reducing waveform ACSLs and preserving waveform similarity can be achieved by adjusting the parameters. Full article

The Nordic hamstring exercise (NHE) is effective at decreasing hamstring strain injury risk. Limited information is available on the in vivo mechanics of the bicep femoris long head (BF_LH) during the NHE. Therefore, the purpose of this study was to observe kinematic, neuromuscular and in-vivo mechanics of the BF_LH during the NHE. Thirteen participants (24.7 ± 3.7 years, 79.56 ± 7.89 kg, 177.40 ± 12.54 cm) performed three repetitions of the NHE at three horizontal planes (0°, 20° and −20°). Dynamic ultrasound of the dominant limb BF_LH, surface electromyography (sEMG) of the contralateral hamstrings and sagittal plane motion data were simultaneously collected. Repeated measures analysis of variance with Bonferroni post hoc corrections were used on the in vivo mechanics and the kinematic and sEMG changes in performance of the NHE. Likely differences in ultrasound waveforms for the BF_LH were determined. Significant and meaningful differences in kinematics and in vivo mechanics between NHE variations were observed. Non-significant differences were observed in sEMG measures between variations. Changes to the NHE performance angle manipulates the lever arm, increasing or decreasing the amount of force required by the hamstrings at any given muscle length, potentially changing the adaptive response when training at different planes and providing logical progressions ore regressions of the NHE. All NHE variations result in a similar magnitude of fascicle lengthening, which may indicate similar positive adaptations from the utilization of any variation. Full article

Channel wave seismic activity often occurs with thin and medium-thick coal seams being the main target layer. To address the problem of channel wave applicability detection in extremely thick coal seams, the propagation and identification characteristics of channel waves remain the focus of research. Therefore, this paper takes the in-seam wave exploration of a 27 m extremely thick coal seam as an example and uses the staggered mesh finite difference method to construct a three-dimensional medium model for numerical simulation. An analysis of the physical parameters of coal and rock, along with the dispersion characteristics of channel waves in extra-thick coal seams, is utilized, through the Zoeppritz equation and the total reflection propagation method, to calculate the imaging. We found the following: (1) The dispersion areas and weak dispersion areas along the detection direction are extremely thick coal seams. (2) There are apparent channel waves in extra-thick coal seams, with a waveform similar to body waves; the length of the wave train is shorter than that of the conventional channel wave, and the arrival time can be estimated accurately. The amplitude of the apparent channel wave is affected by the degree of dispersion, with lower attenuation and higher resolution. The characteristic of total reflection in extremely thick coal seams is that the incident angle is equal to the critical angle, and the dispersion characteristics are weak. (3) The channel waves with weak dispersion characteristics in extra-thick coal seams are mainly Love-type waves. Full article

Through hydrocarbon explorations, deep carbonate reservoirs within a craton were determined to be influenced by deep strike-slip faults, which exhibit small displacements and are challenging to identify. Previous research has established a correlation between seismic attributes and deep geological information, wherein large-scale faults can cause abrupt waveform discontinuities. However, due to the inherent limitations of seismic datasets, such as low signal-to-noise ratios and resolutions, accurately characterizing complex strike-slip faults remains difficult, resulting in increased uncertainties in fault characterization and reservoir prediction. In this study, we integrate advanced techniques such as principal component analysis and structure-oriented filtering with a fault-centric imaging approach to refine the resolution of seismic data from the Tarim craton. Our detailed evaluation encompassed 12 distinct seismic attributes, culminating in the creation of a sophisticated model for identifying strike-slip faults. This model incorporates select seismic attributes and leverages fusion algorithms like K-means, ellipsoid growth, and wavelet transformations. Through the technical approach introduced in this study, we have achieved multi-scale characterization of complex strike-slip faults with throws of less than 10 m. This workflow has the potential to be extended to other complex reservoirs governed by strike-slip faults in cratonic basins, thus offering valuable insights for hydrocarbon exploration and reservoir characterization in similar geological settings. Full article

Objective—Heart rate variability (HRV) and photoplethysmographic waveform variability (PPGV) are available approaches for assessing the state of cardiovascular autonomic regulation. The goal of our study was to compare the frequency-domain features and low-frequency (LF) synchronization of the PPGV and HRV with increasing severity of cardiovascular diseases. Methods—Our study included 998 electrocardiogram (ECG) and finger photoplethysmogram (PPG) recordings from subjects, classified into five categories: 53 recordings from healthy subjects, aged 28.1 ± 6.2 years, 536 recordings from patients with hypertension (HTN), 49.0 ± 8.8 years old, 185 recordings from individuals with stable coronary artery disease (CAD) (63.9 ± 9.3 years old), 104 recordings from patients with myocardial infarction (MI) that occurred three months prior to the recordings (PMI) (65.1 ± 11.0 years old), and 120 recordings from study subjects with acute myocardial infarction (AMI) (64.7 ± 11.5 years old). Spectral analyses of the HRV and PPGV were carried out, along with an assessment of the synchronization strength between LF oscillations of the HRV and of PPGV (synchronization index). Results—Changes in all frequency-domain indices and the synchronization index were observed along the following gradient: healthy subjects → patients with HTN → patients with CAD → patients with PMI → patients with AMI. Similar frequency-domain indices of the PPGV and HRV show little relationship with each other. Conclusions—The frequency-domain indices of the PPGV are highly sensitive to the development of any cardiovascular disease and, therefore, are superior to the HRV indices in this regard. The S index is an independent parameter from the frequency-domain indices. Full article

In this study, we explored the correlation between fluctuated waveform tails under both positive and negative impulse voltages and their corresponding spectral lines during millisecond observations of arc discharge. We examined impulse voltages in ±100, ±125, and ±150 kV across 3, 3.5, and 4 cm gaps using spectroscopic analysis focused on oxygen excitations. Six selected spectra in ±100, ±125, and ±150 kV at 3.5 cm and two negative spectra of −100 kV at 3 and 4 cm were analyzed by identifying spectral lines in the wavelength range of 200–900 nm. The results revealed a correlation between the fluctuated waveform tails and spectral lines in positive voltage discharges, which were almost similar, while in negative voltage discharges, this correlation was found only in −100 kV at 3 and 4 cm. We concluded that during the spark phase for both positive and negative voltage discharges, symmetrical fluctuation in the waveform tails was observed after breakdown time, especially above the voltage level of the recombination phase. This suggested the presence of energetic oxygen excited states in the 200–400 nm range, with higher peak intensity than the O I line at 777.417 nm, observed in most positive impulse voltage discharges and at −100 kV with 3 and 4 cm gaps, contributing to rapid breakdown. Full article

Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual analysis of ECG signals is challenging due to the complex task of identifying and categorizing various waveforms and morphologies within the data. Additionally, ECG datasets often suffer from a significant class imbalance issue, which can lead to inaccuracies in detecting minority class samples. To address these challenges and enhance the effectiveness and efficiency of cardiac arrhythmia detection from imbalanced ECG datasets, this study proposes a novel approach. This research leverages the MIT-BIH arrhythmia dataset, encompassing a total of 109,446 ECG beats distributed across five classes following the Association for the Advancement of Medical Instrumentation (AAMI) standard. Given the dataset’s inherent class imbalance, a 1D generative adversarial network (GAN) model is introduced, incorporating the Bi-LSTM model to synthetically generate the two minority signal classes, which represent a mere 0.73% fusion (F) and 2.54% supraventricular (S) of the data. The generated signals are rigorously evaluated for similarity to real ECG data using three key metrics: mean squared error (MSE), structural similarity index (SSIM), and Pearson correlation coefficient (r). In addition to addressing data imbalance, the work presents three deep learning models tailored for ECG classification: SkipCNN (a convolutional neural network with skip connections), SkipCNN+LSTM, and SkipCNN+LSTM+Attention mechanisms. To further enhance efficiency and accuracy, the test dataset is rigorously assessed using an ensemble model, which consistently outperforms the individual models. The performance evaluation employs standard metrics such as precision, recall, and F1-score, along with their average, macro average, and weighted average counterparts. Notably, the SkipCNN+LSTM model emerges as the most promising, achieving remarkable precision, recall, and F1-scores of 99.3%, which were further elevated to an impressive 99.60% through ensemble techniques. Consequently, with this innovative combination of data balancing techniques, the GAN-SkipNet model not only resolves the challenges posed by imbalanced data but also provides a robust and reliable solution for cardiac arrhythmia detection. This model stands poised for clinical applications, offering the potential to be deployed in hospitals for real-time cardiac arrhythmia detection, thereby benefiting patients and healthcare practitioners alike. Full article

This paper considers a terahertz (THz) dual-function multi-input multi-output (MIMO) radar and communication system with the assistance of a reconfigurable intelligent surface (RIS) and jointly designs the constant modulus (CM) waveform and RIS phase shifts. A weighted optimization scheme is presented, to minimize the weighted sum of three objectives, including communication multi-user interference (MUI) energy, the negative of multi-target illumination power and the MIMO radar waveform similarity error under a CM constraint. For the formulated non-convex problem, a novel alternating coordinate descent (ACD) algorithm is introduced, to transform it into two subproblems for waveform and phase shift design. Unlike the existing optimization algorithms that solve each subproblem by iteratively approximating the optimal solution with iteration stepsize selection, the ACD algorithm can alternately solve each subproblem by dividing it into multiple simpler problems, to achieve closed-form solutions. Our numerical simulations demonstrate the superiorities of the ACD algorithm over the existing methods. In addition, the impacts of the weighting coefficients, RIS and channel conditions on the radar communication performance of the THz system are analyzed. Full article

This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision. The identification of this pattern is used to implement a P300-based speller device, which can serve as an alternative communication channel for persons affected by amyotrophic lateral sclerosis (ALS). This method is validated by identifying this signal by performing a brain–computer interface simulation on a public dataset from ALS patients. Letter identification rates from the speller on the dataset show that this method can identify the P300 signature on the set of 8 patients. The proposed approach achieves similar performance to other state-of-the-art proposals while providing clinically relevant explainability (XAI). Full article

A digitizer is considered one of the fundamental components of an earthquake monitoring system. In this paper, we design and implement a high accuracy seismic digitizer. The implemented digitizer consists of several blocks, i.e., the analog-to-digital converter (ADC), GPS receiver, and microprocessor. Three finite impulse response (FIR) filters are used to decimate the sampling rate of the input seismic data according to user needs. A graphical user interface (GUI) has been designed for enabling the user to monitor the seismic waveform in real time, and process and adjust the parameters of the acquisition unit. The system casing is designed to resist harsh conditions of the environment. The prototype can represent the three component sensors data in the standard MiniSEED format. The digitizer stream seismic data from the remote station to the main center is based on TCP/IP connection. This protocol ensures data transmission without any losses as long as the data still exist in the ring buffer. The prototype was calibrated by real field testing. The prototype digitizer is integrated with the Egyptian National Seismic Network (ENSN), where a commercial instrument is already installed. Case studies shows that, for the same event, the prototype station improves the solution of the ENSN by giving accurate timing and seismic event parameters. Field test results shows that the event arrival time and the amplitude are approximately the same between the prototype digitizer and the calibrated digitizer. Furthermore, the frequency contents are similar between the two digitizers. Therefore, the prototype digitizer captures the main seismic parameters accurately, irrespective of noise existence. Full article

When the electric locomotive pantograph is dropping, the interruption of pantograph catenary contact causes electromagnetic oscillation and arcing. The frequent arc burning that occurs due to charge accumulation results in the amplitude of overvoltage increasing gradually, posing a threat to locomotive high-voltage equipment. However, the physical mechanisms and characteristics of overvoltage are still unclear. This paper proposes a simulation model of operating overvoltage due to a dropping pantograph based on the pantograph–catenary arc and variable capacitance. Distributed RLC electromagnetic oscillation is considered, which allows the real-time calculation of arc resistance and capacitance. Under the same working conditions, the error between the simulation and test results is less than 4.0%, which proves the credibility of the model. The variation law of overvoltage under different dropping speeds or catenary phases was investigated, which shows the max amplitude is 298.20 kV and steepness is 2096.80 kV/μs at 0.30 m/s speed. The waveform shows the characteristics of high amplitude and high steepness, similar to very fast transient overvoltage (VFTO). There is a sinusoidal relationship between the catenary phase and overvoltage amplitude. The closer the catenary phase to 90°, the higher the overvoltage amplitude. The research has important guiding significance for the overvoltage formation mechanism of a traction power supply system and the insulation coordination design of high-voltage equipment. Full article

This article presents a transfer-learning-based method to improve the synthesized speech quality of the low-resource Dungan language. This improvement is accomplished by fine-tuning a pre-trained Mandarin acoustic model to a Dungan language acoustic model using a limited Dungan corpus within the Tacotron2+WaveRNN framework. Our method begins with developing a transformer-based Dungan text analyzer capable of generating unit sequences with embedded prosodic information from Dungan sentences. These unit sequences, along with the speech features, provide <unit sequence with prosodic labels, Mel spectrograms> pairs as the input of Tacotron2 to train the acoustic model. Concurrently, we pre-trained a Tacotron2-based Mandarin acoustic model using a large-scale Mandarin corpus. The model is then fine-tuned with a small-scale Dungan speech corpus to derive a Dungan acoustic model that autonomously learns the alignment and mapping of the units to the spectrograms. The resulting spectrograms are converted into waveforms via the WaveRNN vocoder, facilitating the synthesis of high-quality Mandarin or Dungan speech. Both subjective and objective experiments suggest that the proposed transfer learning-based Dungan speech synthesis achieves superior scores compared to models trained only with the Dungan corpus and other methods. Consequently, our method offers a strategy to achieve speech synthesis for low-resource languages by adding prosodic information and leveraging a similar, high-resource language corpus through transfer learning. Full article

Electromyostimulation (EMS) is an up-and-coming training method that demands further fundamental research regarding its safety and efficacy. To investigate the influence of different stimulation parameters, electrode positions and electrode sizes on the resulting voltage in the tissue, a tissue mimicking phantom is needed. Therefore, this study describes the fabrication of a hydrogel arm phantom for EMS applications with the tissue layers of skin, fat, blood and muscle. The phantom was dielectrically validated in the frequency range of 20 Hz to 100 Hz. We also conducted electromyography (EMG) recordings during EMS on the phantom and compared them with the same measurements on a human arm. The phantom reproduces the dielectric properties of the tissues with deviations ranging from 0.8% to more than 100%. Although we found it difficult to find a compromise between mimicking the permittivity and electrical conductivity at the same time, the EMS–EMG measurements showed similar waveforms (1.9–9.5% deviation) in the phantom and human. Our research contributes to the field of dielectric tissue phantoms, as it proposes a multilayer arm phantom for EMS applications. Consequently, the phantom can be used for initial EMS investigations, but future research should focus on further improving the dielectric properties. Full article

Compelling evidence has shown that geomagnetic disturbances in vertical intensity polarization before great earthquakes are promising precursors across diverse rupture conditions. However, the geomagnetic vertical intensity polarization method uses the spectrum of smooth signals, and the anomalous waveforms of seismic electromagnetic radiation, which are basically nonstationary, have not been adequately considered. By combining pulse amplitude analysis and an experimental study of the cumulative frequency of anomalies, we found that the pulse amplitudes before the 2022 Luding M6.8 earthquake show characteristics of multiple synchronous anomalies, with the highest (or higher) values occurring during the analyzed period. Similar synchronous anomalies were observed before the 2021 Yangbi M6.4 earthquake, the 2022 Lushan M6.1 earthquake and the 2022 Malcolm M6.0 earthquake, and these anomalies indicate migration from the periphery toward the epicenters over time. The synchronous changes are in line with the recognition of previous geomagnetic anomalies with characteristics of high values before an earthquake and gradual recovery after the earthquake. Our study suggests that the pulse amplitude is effective for extracting anomalies in geomagnetic vertical intensity polarization, especially in the presence of nonstationary signals when utilizing observations from multiple station arrays. Our findings highlight the importance of incorporating pulse amplitude analysis into earthquake prediction research on geomagnetic disturbances. Full article