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Polycrystalline magnesium aluminate spinel (PMAS) ceramics is a kind of transparent oxide ceramic with the advantages of high hardness, high mechanical strength, thermal stability and chemical corrosion resistance. The optical transmittance and infrared absorption edge of PMAS ceramic are better than that of sapphire and ALON in the mid-infrared band with the wavelengths greater than 4.5 μm, which makes it as an ideal material for manufacturing the optics used in the fields of transparent armor protection, infrared optical window and infrared missile dome. However, the hardness of PMAS ceramics is close to that of sapphire, and the brittleness is greater than that of sapphire, which brings great challenges to ultra-precision ductile grinding. In this paper, the surface characteristics and damage mechanisms in ultra-precision grinding of PMAS ceramics were systematically researched based on AE signals monitoring. Firstly, the effects of grinding depth, feed speed and grain size on the surface characteristics were studied in detail. Secondly, the influences of grinding depth and feed speed on the damage characteristics of PMAS ceramics were researched based on energy spectrum analysis of AE signals. Thirdly, the evolution mechanisms of material removal modes and damage characteristics were revealed based on time-frequency analysis of AE signals. Then, the damage types on ductile surfaces and initiation mechanisms of damage crack were investigated. Ultimately, the crack-free ductile surfaces and surface roughness (Sa) of 3 nm were achieved through ultra-precision grinding. [ABSTRACT FROM AUTHOR]

Nonstationary fault signals collected from wind turbine planetary gearboxes and bearings often exhibit close-spaced instantaneous frequencies (IFs), or even crossed IFs, bringing challenges for existing time-frequency analysis (TFA) methods. To address the issue, a data-driven TFA technique, termed CTNet is developed. The CTNet is a novel model that combines a fully convolutional auto-encoder network with the convolutional block attention module (CBAM). In the CTNet, the encoder layer is first designed to extract coarse features of the time-frequency representation (TFR) calculated by the general linear Chirplet transform (GLCT); second, the decoder layer is combined to restore and conserve details of the key time-frequency features; third, the skip connections are designed to accelerate training by linking extracted and reconstructed features; finally, the CBAM is introduced to adaptively explore channel and spatial relationships of the TFR, focusing more on close-spaced or crossed frequency features, and effectively reconstruct the TFR. The effectiveness of the CTNet is validated by numerical signals with close-spaced or crossed IFs, and real-world signals of wind turbine planetary gearbox and bearings. Comparison analysis with state-of-the-art TFA methods shows that the CTNet has high time-frequency resolution in characterizing nonstationary signals and a much better ability to detect wind turbine faults. ● A data-driven TFA technique is proposed for fault diagnosisof wind turbine planetary gearboxes and bearings. ● The proposed CTNet is a novel model that combines a convolutional network with the attention mechanism. ● The effectiveness of the CTNet is validated through the multiple sets of simulated and real-world signals. ● The CTNet can effectively characterize signals with close-spaced or crossed IFs and detect wind turbine faults. [ABSTRACT FROM AUTHOR]

Vibration is inevitable in operation of mechanical equipment, but severe vibrations will cause serious harm to wind turbines (WTs). This paper takes the drivetrain of 8 MW WT as the object, established a multi-body electromechanical coupling model of WT and verified the accuracy of the established model through the WT drivetrain in the laboratory. Subsequently, based on this model, time-frequency domain analysis was conducted on the vibration response of the WT drivetrain under different control strategies of the generator, and the influence of the generator control strategy on the vibration of the drivetrain was studied. The result shows that the generator control strategy has a significant impact on the vibration response amplitude of the drivetrain, but has a relatively small impact on the frequency component. Meanwhile, the sliding mode controller designed in this paper has good anti-interference ability, which can make the system quickly reach dynamic stability, and effectively suppress the vibration of the main components in the drivetrain. Under the new controller's control, the transverse vibration displacement of some parts of the WT drivetrain can be reduced by up to nearly 60 %. This paper provides new ideas and new methods for the active vibration reduction control of WT drivetrain. [ABSTRACT FROM AUTHOR]

[Display omitted] • The height disadvantage promotes third-party punishment (TPP). • Third-parties are influenced by negative emotions resulting from height disadvantage. • The N1, P3 components are linked to the influence of height disadvantage on TPP. • The height disadvantage also evoked more theta power, and more alpha power. The study employed event-related potential (ERP), time–frequency analysis, and functional connectivity to comprehensively explore the influence of male's relative height on third-party punishment (TPP) and its underlying neural mechanism. The results found that punishment rate and transfer amount are significantly greater when the height of the third-party is lower than that of the recipient, suggesting that male's height disadvantage promotes TPP. Neural results found that the height disadvantage induced a smaller N1. The height disadvantage also evoked greater P300 amplitude, more theta power, and more alpha power. Furthermore, a significantly stronger wPLI between the rTPJ and the posterior parietal and a significantly stronger wPLI between the DLPFC and the posterior parietal were observed when third-party was at the height disadvantage. These results imply that the height disadvantage causes negative emotions and affects the fairness consideration in the early processing stage; the third-party evaluates the blame of violators and makes an appropriate punishment decision later. Our findings indicate that anger and reputation concern caused by height disadvantage promote TPP. The current study holds significance as it underscores the psychological importance of height in males, broadens the perspective on factors influencing TPP, validates the promoting effect of personal disadvantages on prosocial behavior, enriches our understanding of indirect reciprocity theory, and extends the application of the evolution theory of Napoleon complex. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases (NDDs) pose significant challenges due to their debilitating nature and limited therapeutic options. Accurate and timely diagnosis is crucial for optimizing patient care and treatment strategies. Gait analysis, utilizing wearable sensors, has shown promise in assessing motor abnormalities associated with NDDs. Research Question 1 To what extent can analyzing the interaction of both limbs in the time-frequency domain serve as a suitable methodology for accurately classifying NDDs? Research Question 2 How effective is the utilization of color-coded images, in conjunction with deep transfer learning models, for the classification of NDDs? GaitNDD database was used, comprising recordings from patients with Huntington's disease, amyotrophic lateral sclerosis, Parkinson's disease, and healthy controls. The gait signals underwent signal preparation, wavelet coherence analysis, and principal component analysis for feature enhancement. Deep transfer learning models (AlexNet, GoogLeNet, SqueezeNet) were employed for classification. Performance metrics, including accuracy, sensitivity, specificity, precision, and F1 score, were evaluated using 5-fold cross-validation. The classification performance of the models varied depending on the time window used. For 5-second gait signal segments, AlexNet achieved an accuracy of 95.91 %, while GoogLeNet and SqueezeNet achieved accuracies of 96.49 % and 92.73 %, respectively. For 10-second segments, AlexNet outperformed other models with an accuracy of 99.20 %, while GoogLeNet and SqueezeNet achieved accuracies of 96.75 % and 95.00 %, respectively. Statistical tests confirmed the significance of the extracted features, indicating their discriminative power for classification. The proposed method demonstrated superior performance compared to previous studies, offering a non-invasive and cost-effective approach for the automated diagnosis of NDDs. By analyzing the interaction between both legs during walking using wavelet coherence, and utilizing deep transfer learning models, accurate classification of NDDs was achieved. • Wearable sensors detect motor abnormalities in neurodegenerative diseases. • Limb interaction & color-coded images achieve accurate NDD classification. • A non-invasive & cost-effective approach for early diagnosis & treatment planning. [ABSTRACT FROM AUTHOR]

To explore the association between insomnia symptoms and cognitive flexibility among undergraduates, along with its potential neural mechanisms. A total of 102 participants were divided into insomnia (n = 55) and control (n = 47) groups based on sleep status. Cognitive flexibility was assessed using the Cognitive Flexibility Inventory (CFI) and the Number-Letter Task (N-L task). EEG data were recorded during the N-L task. The insomnia group exhibited lower CFI scores and higher switch costs in reaction time and accuracy compared to the control group. ERP analysis showed differences in P2, N2, and P3 component amplitudes between the two groups, with reduced N2 amplitude in the insomnia group under repeat trials. Time-frequency analysis revealed larger theta band event related synchronization in the frontal region and smaller theta band ERS in the parietal region under switch trials in the control group; the alpha band event-related desynchronization in the parietal region under repeat trials was significantly smaller in the control group compared to switch trials. Compared to undergraduates with normal sleep, those with insomnia symptoms exhibited reduced cognitive flexibility, which may be associated with some alterations in brain electrophysiological activities. • Insomnia symptoms was associated with reduced cognitive flexibility. • Neural mechanism for above difference may due to frontal and parietal dysfunctions. • We integrated multiple measures to assess the differences in cognitive flexibility. [ABSTRACT FROM AUTHOR]

• In the time–frequency analysis, P25 and N35 showed power increases of high-frequency activities in comparison to baseline activities, while P50 showed a power decrease. • Short-latency of giant SEPs (P25 and N35) reflect evoked paroxysmal depolarization shifts. • Middle latency of giant SEPs (P50) reflects the inhibitory component. Giant somatosensory evoked potentials (SEPs) are observed in patients with cortical myoclonus. Short-latency components (SLC), are regarded as evoked epileptic activities or paroxysmal depolarization shifts (PDSs). This study aimed to reveal the electrophysiological significance of the middle-latency component (MLC) P50 of the SEPs. Twenty-two patients with cortical myoclonus having giant SEPs (patient group) and 15 healthy controls were included in this study. Waveform changes in SEPs before and after perampanel (PER) treatment were evaluated in the patient group. The wide range, time–frequency properties underlying the waveforms were compared between the groups. After PER treatment, SLC was prolonged and positively correlated with PER concentration, whereas MLC showed no correlation with PER concentration. Time–frequency analysis showed a power increase (156 Hz in all patients, 624 Hz in benign adult familial myoclonus epilepsy patients) underlying SLC and a power decrease (156 Hz, 624 Hz) underlying MLC in the patient group. The high-frequency power increase in SLCs and decrease in MLCs clearly reflected PDS and subsequent hyperpolarization, respectively. This relationship was similar to that of interictal epileptiform discharges, suggesting that giant SEPs evoke epileptic complexes of excitatory and inhibitory components. MLCs of giant SEPs reflected inhibitory components. [ABSTRACT FROM AUTHOR]

Cerebral blood flow (CBF) autoregulation is the physiologic process whereby blood supply to the brain is kept constant over a range of cerebral perfusion pressures ensuring a constant supply of metabolic substrate. Clinical methods for monitoring CBF autoregulation were first developed for neurocritically ill patients and have been extended to surgical patients. These methods are based on measuring the relationship between cerebral perfusion pressure and surrogates of CBF or cerebral blood volume (CBV) at low frequencies (<0.05 Hz) of autoregulation using time or frequency domain analyses. Initially intracranial pressure monitoring or transcranial Doppler assessment of CBF velocity was utilised relative to changes in cerebral perfusion pressure or mean arterial pressure. A more clinically practical approach utilising filtered signals from near infrared spectroscopy monitors as an estimate of CBF has been validated. In contrast to the traditional teaching that 50 mm Hg is the autoregulation threshold, these investigations have found wide interindividual variability of the lower limit of autoregulation ranging from 40 to 90 mm Hg in adults and 20–55 mm Hg in children. Observational data have linked impaired CBF autoregulation metrics to adverse outcomes in patients with traumatic brain injury, ischaemic stroke, subarachnoid haemorrhage, intracerebral haemorrhage, and in surgical patients. CBF autoregulation monitoring has been described in both cardiac and noncardiac surgery. Data from a single-centre randomised study in adults found that targeting arterial pressure during cardiopulmonary bypass to above the lower limit of autoregulation led to a reduction of postoperative delirium and improved memory 1 month after surgery compared with usual care. Together, the growing body of evidence suggests that monitoring CBF autoregulation provides prognostic information on eventual patient outcomes and offers potential for therapeutic intervention. For surgical patients, personalised blood pressure management based on CBF autoregulation data holds promise as a strategy to improve patient neurocognitive outcomes. [ABSTRACT FROM AUTHOR]

Response inhibition is a core cognitive impairment in bipolar disorder (BD), leading to increased impulsivity in BD. However, the relationship between the neural mechanisms underlying impaired response inhibition and impulsivity in BD is not yet clear. Individuals who are genetically predisposed to BD give a way of identifying potential endophenotypes. A total of 97 participants, including 39 patients with BD, 22 unaffected relatives (UR) of patients with BD, and 36 healthy controls performed a Go/No-Go task during magnetoencephalography. We carried out time-frequency and connectivity analysis on MEG data. Decreased beta power, prolonged latency and increased peak frequency in rIFG, decreased beta power in pre-SMA and reduced rIFG-to-pre-SMA connectivity were found in BD relative to healthy controls. In the UR group, we found a decrease in the beta power of pre-SMA and prolonged latency of rIFG. Furthermore, increased motor impulsiveness in BD was related to abnormal alterations in beta oscillatory activity of rIFG and functional connectivity between rIFG and pre-SMA. Hypoactivity activity in rIFG and impaired dominant role of rIFG in the prefrontal control network may underlie the neuropathology of response inhibition dysfunction, resulting increased motor impulsivity in BD. Our findings point to measuring rIFG dysfunction as a potential means of identifying individuals at genetic high risk for transition to BD disease expression. • Reduced pre-SMA activation and prolonged rIFG activation relate to genetic risk of BD. • Blunted activation in rIFG and attenuated connectivity from rIFG to pre-SMA reflect disease expression for BD. • Hypofunction in rIFG and disconnectivity with pre-SMA during response inhibition are associated with motor impulsivity in BD. [ABSTRACT FROM AUTHOR]

Most of the signals in the field of industrial engineering are nonstationary signals, and the accurate description of the time-frequency (TF) characteristics of nonstationary signals is important for the advancement of industrial engineering. Instantaneous frequency (IF) and group delay (GD) are common TF features used to describe nonstationary signals. Time-reassigned synchrosqueezing transform and synchrosqueezing transform are two TF analysis tools that can accurately characterize the GD and IF of nonstationary signals by squeezing the TF coefficients in the time direction and frequency direction, respectively. However, it is difficult for such two techniques to accurately characterize IF and GD simultaneously. A novel method called time-frequency squeezing transform is introduced in this paper to conquer this drawback. The technique first uses the short-time Fourier transform to calculate the time-frequency representation (TFR) of a signal, and then divides the TFR into two parts according to a chirp rate estimator. The subdivided TFR parts are then squeezed in the frequency and time directions to accurately characterize the IF and GD, respectively, and the two squeezed results are added to form a high-resolution result. The effectiveness of the proposed technique is demonstrated with numerical and experimental signals. • A novel time-frequency analysis technique called time-frequency squeezing transform (TFST) which enables efficient and high-resolution time-frequency analysis. • The advantage of TFST is that it can adaptively select the appropriate processing direction for the harmonic-like and impulsive-like components of the signal, thus generating energy-concentrated TFRs for all components of the signal. • The proposed method can separate the harmonic-like component from the impulsive-like component in the signal. • The technique can reconstruct the original signal and has good robustness to noise. • The numerical and experimental examples are used to validate the proposed method. [ABSTRACT FROM AUTHOR]

• Transient bursts in the single-trial EEG are the building blocks of averaged continuous brain oscillations during photic driving. • Reported fluctuations of averaged continuous brain oscillations can be explained by the occurrence of transient burst events. • Entrainment and resonance effects occur in single-trial bursts and averaged data. Photic driving in the human visual cortex evoked by intermittent photic stimulation is usually characterized in averaged data by an ongoing oscillation showing frequency entrainment and resonance phenomena during the course of stimulation. We challenge this view of an ongoing oscillation by analyzing unaveraged data. 64-channel EEGs were recorded during visual stimulation with light flashes at eight stimulation frequencies between 7.8 and 23 Hz for fourteen healthy volunteers. Time-frequency analyses were performed in averaged and unaveraged data. While we find ongoing oscillations in the averaged data during intermittent photic stimulation, we find transient events (bursts) of activity in the unaveraged data. Both resonance and entrainment occur for the ongoing oscillations in the averaged data and the bursts in the unaveraged data. We argue that the continuous oscillations in the averaged signal may be composed of brief, transient bursts in single trials. Our results can also explain previously observed amplitude fluctuations in averaged photic driving data. Single-trial analyses might consequently improve our understanding of resonance and entrainment phenomena in the brain. [ABSTRACT FROM AUTHOR]

This study used event-related potential (ERP) and resting-state functional connectivity (rs-FC) approaches to investigate the neural mechanisms underlying the emotional attention bias in patients with chronic insomnia disorder (CID). Twenty-five patients with CID and thirty-three demographically matched healthy controls (HCs) completed clinical questionnaires and underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) scans. EEG analysis examined the group differences in terms of reaction times, P3 amplitudes, event-related spectral perturbations, and inter-trial phase synchrony. Subsequently, seed-based rs-FC analysis of the amygdala nuclei (including the central-medial amygdala [CMA] and basolateral amygdala [BLA]) was performed. The relationship between P3 amplitude, rs-FC and clinical symptom severity in patients with CID was further investigated by correlation analysis. CID patients exhibited shorter reaction times than HCs in both standard and deviant stimuli, with the abnormalities becoming more pronounced as attention allocation increased. Compared to HCs, ERP analysis revealed increased P3 amplitude, theta wave power, and inter-trial synchrony in CID patients. The rs-FC analysis showed increased connectivity of the BLA-occipital pole, CMA-precuneus, and CMA-angular gyrus and decreased connectivity of the CMA-thalamus in CID patients. Notably, correlation analysis of the EEG and fMRI measurements showed a significant positive correlation between the P3 amplitude and the rs-FC of the CMA-PCU. This study confirms an emotional attention bias in CID, specifically in the neural mechanisms of attention processing that vary depending on the allocation of attentional resources. Abnormal connectivity in the emotion-cognition networks may constitute the neural basis of the abnormal scalp activation pattern. [ABSTRACT FROM AUTHOR]

We investigate the response of the topside ionosphere, auroral and polar sectors, to the forcing of the geospace during September 2017. Specifically, we aim at characterizing such a response in terms of the involved spatial scales and of their intensification during the different auroral and polar cap activity conditions experienced in the selected month, that is characterized by severe geomagnetic storm conditions. For our purposes, we leverage on and compare various in situ plasma density data products provided by the Swarm constellation of the European Space Agency (ESA). The spatio-temporal variability of the involved scales in the plasma density observation is featured through the application of the Fast Iterative Filtering (FIF) signal decomposition technique and, for the first time in the ionospheric field, of a FIF-derived dynamical spectrum called "IMFogram". The instantaneous time-frequency representation provided through the IMFogram illustrates the time development of the multi-scale processes with spatial and temporal resolutions higher than those obtained with traditional signal processing techniques. To demonstrate this, the IMFogram is tested against Fast Fourier and Continuous Wavelet Transforms. With our fine characterization, we highlight how scale cascading and intensification processes in the plasma density observations follow the ionospheric currents activity, as depicted through the auroral activity and polar cap indices, and through the field-aligned currents data product provided by Swarm. [ABSTRACT FROM AUTHOR]

• We investigated the neural mechanisms underlying the influence of intimate relationships on female competitive regulation. • The P1, N4, and LPP components are associated with the impact of intimate relationships on female competition. • The alpha frequency band is related to attentional control during female competition. • The strength of functional connections in women's brains is related to their level of participation in competition. Competition, an essential component of social interaction, frequently occurs in daily life, and the impact of intimate relationships on women's competition has not yet been revealed. In this study, the visual target paradigm was used to explore the neural mechanisms underlying the regulation of female competitiveness by intimate relationships using event-related potential (ERP) data, time–frequency analysis, and brain functional connectivity. The research results indicate that, the P1, the N4, and the LPP were sensitive to the impact of intimate relationships on competition. Compared to competition between unfamiliar opposite-gender dyads, the average amplitudes of the N4 and LPP were higher in the late stage of competition between romantic partners. Compared to competition with strangers of the opposite gender, alpha band power was significantly higher when female individuals competed with their romantic partners. In addition, there was a positive correlation between the synchronization of activity in the frontal, parietal, and right temporal lobes of a female's brain and their degree of female engagement in competition. When a female individuals focused on the competition, activity synchronization was higher. The results indicate that competition with unfamiliar individuals of the opposite gender can make female focus on the competitive task, causing synchronous activation of corresponding brain regions. When competing with a romantic partner, women's focus decreases, their willingness to compete decreases, and the synchrony of brain functional connectivity decreases. This study suggests that intimate relationship weakens women's competitiveness, which is of significant importance for understanding high-quality intimate relationship and promoting the development of healthy competition. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the neural mechanisms underlying working memory impairment in patients with chronic insomnia disorder (CID) using event-related potentials (ERP) and resting-state functional connectivity (rsFC) approaches. Participants, including CID patients and healthy controls (HCs), completed clinical scales and underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). EEG analysis compared reaction times, P3 amplitudes, event-related spectral perturbations (ERSP), and inter-trial phase synchronisation (ITPS) between CID patients and HCs. Subsequently, frontal regions (i.e., the Superior Frontal Gyrus [SFG] and Middle Frontal Gyrus [MFG]) corresponding to the EEG were selected as seeds for rsFC analysis. Correlation analyses were conducted to further investigate the relationship between functional connectivity abnormalities in brain regions and clinical symptom severity and P3 amplitude in CID patients. Compared to HCs, CID patients exhibited slower reaction times across all working memory conditions, with the deficits becoming more pronounced as memory load increased. ERP analysis revealed increased P3 amplitude, theta wave power, and reduced inter-trial synchrony in CID patients. rsFC analysis showed decreased connectivity of SFG-posterior cingulated cortex (PCC), SFG-MFG, and MFG-frontal pole (FP), and increased connectivity of MFG- Middle Temporal Gyrus (MTG)in CID patients. Importantly, a significant correlation was found between the rsFC of SFG-MTG and P3 amplitude during 1-back. This study confirms deficits in working memory capacity in patients with CID, specifically in the neural mechanisms of cognitive processing that vary depending on the level of cognitive load. Alterations in connectivity patterns within and between the frontal and temporal regions may be the neural basis of the cognitive impairment. • Patients with CID exhibited working memory deficits and altered cognitive load management. • Alterations in connectivity patterns within and between the frontal and temporal regions may underlie working memory deficits. • The integration of ERP and fMRI explored the potential mechanisms of working memory deficitsfrom a multimodal perspective. [ABSTRACT FROM AUTHOR]

We present a different symplectic point of view in the definition of weighted modulation spaces M m p , q (R d) and weighted Wiener amalgam spaces W (F L m 1 p , L m 2 q) (R d). All the classical time-frequency representations, such as the short-time Fourier transform (STFT), the τ -Wigner distributions and the ambiguity function, can be written as metaplectic Wigner distributions μ (A) (f ⊗ g ¯) , where μ (A) is the metaplectic operator and A is the associated symplectic matrix. Namely, time-frequency representations can be represented as images of metaplectic operators, which become the real protagonists of time-frequency analysis. In [13] , the authors suggest that any metaplectic Wigner distribution that satisfies the so-called shift-invertibility condition can replace the STFT in the definition of modulation spaces. In this work, we prove that shift-invertibility alone is not sufficient, but it has to be complemented by an upper-triangularity condition for this characterization to hold, whereas a lower-triangularity property comes into play for Wiener amalgam spaces. The shift-invertibility property is necessary: Rihaczek and conjugate Rihaczek distributions are not shift-invertible and they fail the characterization of the above spaces. We also exhibit examples of shift-invertible distributions without upper-triangularity condition which do not define modulation spaces. Finally, we provide new families of time-frequency representations that characterize modulation spaces, with the purpose of replacing the time-frequency shifts with other atoms that allow to decompose signals differently, with possible new outcomes in applications. [ABSTRACT FROM AUTHOR]

In complex and diverse social circumstances, decision making is affected by social feedback. Although previous studies have examined the electrophysiological correlates of social feedback with a binary valence, those related to non-binary feedback, or the magnitude of social feedback, remain unclear. This study investigated the electrophysiological correlates of non-binary social feedback and subsequent action selection processing. Participants were asked to complete a Gabor patch direction judgment task in which they were required to make judgments before and after receiving social feedback. They were informed that the feedback stimuli represented the degree to which other participants made the same choice. The results revealed that feedback that was highly concordant with the participant's judgments elicited greater P300 activity, which was associated with the fulfillment of expectations regarding social reward. Moreover, moderately concordant feedback induced stronger theta band power, which may indicate monitoring of subjective conflict. Temporal changes in theta power during feedback phase may also relate to adjustments in prediction error. Additionally, when an initial judgment was maintained following social feedback, we observed a stronger increase in beta power, indicating an association with post-social-feedback action processing. • This study aims to investigate electrophysiological evidence for processing non-binary magnitudes of social agreement. • We employed a series of pseudo-gradually changing natural numbers to represent non-binary social feedback. • Using CBPT, we observed electrophysiological correlates linked to non-binary social feedback and following action selection. [ABSTRACT FROM AUTHOR]

Depression, as a global mental health problem, is lacking effective screening methods that can help with early detection and treatment. This paper aims to facilitate the large-scale screening of depression by focusing on the speech depression detection (SDD) task. Currently, direct modeling on the raw signal yields a large number of parameters, and the existing deep learning-based SDD models mainly use the fixed Mel-scale spectral features as input. However, these features are not designed for depression detection, and the manual settings limit the exploration of fine-grained feature representations. In this paper, we learn the effective representations of the raw signals from an interpretable perspective. Specifically, we present a joint learning framework with attention-guided learnable time-domain filterbanks for depression classification (DALF), which collaborates with the depression filterbanks features learning (DFBL) module and multi-scale spectral attention learning (MSSA) module. DFBL is capable of producing biologically meaningful acoustic features by employing learnable time-domain filters, and MSSA is used to guide the learnable filters to better retain the useful frequency sub-bands. We collect a new dataset, the Neutral Reading-based Audio Corpus (NRAC), to facilitate the research in depression analysis, and we evaluate the performance of DALF on the NRAC and the public DAIC-woz datasets. The experimental results demonstrate that our method outperforms the state-of-the-art SDD methods with an F1 of 78.4% on the DAIC-woz dataset. In particular, DALF achieves F1 scores of 87.3% and 81.7% on two parts of the NRAC dataset. By analyzing the filter coefficients, we find that the most important frequency range identified by our method is 600–700Hz, which corresponds to the Mandarin vowels / e / and / e ˆ / and can be considered as an effective biomarker for the SDD task. Taken together, our DALF model provides a promising approach to depression detection. • A DFBL module is proposed to replace the Mel-spectral and offer interpretability. • A MSSA module is developed to guide the filters in learning the important sub-bands. • We collect a Chinese corpus and verify method's validity by extensive experiments. [ABSTRACT FROM AUTHOR]

The periodic generalized harmonic wavelet (PGHW) method is used to analyze the forced response of a periodic time-varying rotor-blade nonlinear system undergoing mechanical vibrations. The non-stationary non-Gaussian stochastic process with non-uniform modulation is considered as external excitation and the typical quadratic form is considered in the nonlinear terms. The mechanical model of the rotor-blade system and the theoretical and numerical PGHW are introduced. The trigonometric functions contained in M (t) , C (t) , K (t) can be transformed into exponential functions according to the Euler formula, and then these time-varying elements can result in a frequency shift. The equation of motion of the rotor-blade system with a periodic time-varying form derived using Lagrangian dynamics can be transformed into a series of algebraic equations by using the wavelet-Galerkin method. The wavelet coefficients of response can be solved by using the quasi-Newton method, and the single-rank inverse Broyden algorithm is used to approximate the Jacobian matrix. Based on these wavelet coefficients, the displacement of response can be obtained by performing the reconstruction of PGHW, and the estimated evolutionary power spectral density (EPSD) of response can be obtained according to Eq. (15). The time-frequency analysis is shown in the numerical example, Runge-Kutta algorithm and Monte Carlo method are used to verify the feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

• Human awareness detection dynamically reacts to high-altitude environment. • Early and late components co-mediate the reaction with exposure duration. • Opposite change of two components as a response to the reduced cognitive resources. • Increased late component and fast reaction resist high-altitude exposure. The psychological effects of long-term exposure to high-altitude environments have attracted great attention. These effects are usually attributed to the diminished cognitive resources due to high-altitude exposure. This study employed electroencephalography (EEG) to investigate the effects of exposure duration on awareness detection tasks. Neither reaction time nor accuracy showed the direct effects of the exposure duration, so did the model indexes obtained from drift diffusion model analysis. However, event-related potentials (ERP) analysis revealed that exposure duration was associated with changes in the visual awareness negativity (VAN) and the late positivity (LP) components, which in turn affected reaction time. Specifically, longer exposure durations were associated with lower VAN and higher LP, resulting in shorter reaction times and greater drift rate. In contrast to previous studies, the reverse relationship between VAN and LP may reflect a compensatory response to the reduced cognitive resources caused by high-altitude exposure. Additionally, increased LP and shorter reaction times with exposure duration may reflect a resistance to the high-altitude environment. We also conducted time–frequency analysis and found that theta power did not vary with exposure duration, suggesting that the reduction in cognitive resources remains stable in these individuals over time. Overall, our study provides new insights into the dynamic effects of high-altitude environments on awareness detection in the presence of reduced cognitive resources. [ABSTRACT FROM AUTHOR]

• A clear directionality of somatosensory evoked potentials recorded from segmented DBS leads in the sensory thalamus is observed. • This directional effect provides further evidence in support of the somatotopy of the sensory thalamus. • This may help identify the neurophysiological sweet spot in the possibly reorganized sensory thalamus in chronic pain patients. The aim of this feasibility study was to investigate the properties of median nerve somatosensory evoked potential (SEPs) recorded from segmented Deep Brain Stimulation (DBS) leads in the sensory thalamus (VP) and how they relate to clinical and anatomical findings. We analyzed four patients with central post-stroke pain and DBS electrodes placed in the VP. Median nerve SEPs were recorded with referential and bipolar montages. Electrode positions were correlated with thalamus anatomy and tractography-based medial lemniscus. Early postoperative clinical paresthesia mapping was performed by an independent pain nurse. Finally, we performed frequency and time–frequency analyses of the signals. We observed differences of SEP amplitudes recorded along different directions in the VP. SEP amplitudes did not clearly correlate to both atlas-based anatomical position and fiber-tracking results of the medial lemniscus. However, the contacts of highest SEP amplitude correlated with the contacts of lowest effect-threshold to induce paraesthesia. SEP recordings from directional DBS leads offer additional information about the neurophysiological (re)organization of the sensory thalamus. Directional recordings of thalamic SEPs bear the potential to assist clinical decision-making in DBS for pain. [ABSTRACT FROM AUTHOR]

The extent to which advanced waveform analysis of non-invasive physiological signals can diagnose levels of hypovolemia remains insufficiently explored. The present study explores the discriminative ability of a deep learning (DL) framework to classify levels of ongoing hypovolemia, simulated via novel dynamic lower body negative pressure (LBNP) model among healthy volunteers. We used a dynamic LBNP protocol as opposed to the traditional model, where LBNP is applied in a predictable step-wise, progressively descending manner. This dynamic LBNP version assists in circumventing the problem posed in terms of time dependency, as in real-life pre-hospital settings intravascular blood volume may fluctuate due to volume resuscitation. A supervised DL-based framework for ternary classification was realized by segmenting the underlying noninvasive signal and labeling segments with corresponding LBNP target levels. The proposed DL model with two inputs was trained with respective time–frequency representations extracted on waveform segments to classify each of them into blood volume loss: Class 1 (mild); Class 2 (moderate); or Class 3 (severe). At the outset, the latent space derived at the end of the DL model via late fusion among both inputs assists in enhanced classification performance. When evaluated in a 3-fold cross-validation setup with stratified subjects, the experimental findings demonstrated PPG to be a potential surrogate for variations in blood volume with average classification performance, AUROC: 0.8861, AUPRC: 0.8141, F 1 -score:72.16%, Sensitivity:79.06%, and Specificity:89.21%. Our proposed DL algorithm on PPG signal demonstrates the possibility to capture the complex interplay in physiological responses related to both bleeding and fluid resuscitation using this challenging LBNP setup. [ABSTRACT FROM AUTHOR]

• This is a review of methods used to analyze speech signals for automatic recognition of their associated text, speaker identity, and other pertinent information are reviewed. • The survey focuses on the requirements of different applications, as diverse speech tasks have often used the same methods, despite having very different objectives. • As relevant information in a speech signal is distributed highly non-uniformly, a wide variety of time and frequency analysis techniques is examined. • The utility of methods is noted in terms of performance, using accuracy, complexity, cost, and latency as measures. This paper reviews methods used to analyze speech signals for various applications such as automatic recognition of associated text and speaker identity, and coding. The survey focuses on the requirements of different applications, as diverse speech tasks have often used the same methods, despite having very different objectives. As relevant information in a speech signal is distributed highly non-uniformly, a variety of time and frequency analysis techniques is examined. The utility of methods is noted in terms of performance, using accuracy, complexity, cost, and latency as criteria. [ABSTRACT FROM AUTHOR]

Response monitoring, as reflected in electroencephalogram recordings after commission of errors, has been consistently shown to be abnormally enhanced in individuals with obsessive-compulsive disorder (OCD). This has traditionally been quantified as error-related negativity (ERN) and may reflect abnormal neurophysiological mechanisms underlying OCD. However, the ERN reflects the increase in phase-locked activities, particularly in the theta-band (4–8 Hz), and does not reflect non–phase-locked activities. To more broadly investigate midfrontal theta activity in a brain region that is essential for complex cognition, this study investigated theta abnormalities during response monitoring in participants with OCD to acheive a better understanding of the mechanism underlying the ERN. Electroencephalogram data were recorded from 99 participants with pediatric OCD and 99 sex- and age-matched healthy control participants while they completed the arrow flanker task. Effects of group (OCD, healthy control) and response type (error, correct) on postresponse theta total power and intertrial phase coherence (ITPC) were examined using mixed analysis of covariance and Bayesian analyses controlling for sex and accuracy. Theta total power was larger on error than on correct trials and larger in OCD than healthy control participants, but there was no effect of response type between groups. Theta ITPC was larger on error than correct trials, but there was no group difference or response type difference between the groups. Correlations of theta total power and ITPC with clinical measures were overall small. Abnormally enhanced midfrontal theta total power, but not ITPC, may reflect ineffective heightened response monitoring or compensatory activity in pediatric OCD. [ABSTRACT FROM AUTHOR]

Time–frequency analysis methods can be used to characterize the time-varying characteristics of a signal. The postprocessing algorithm further enhances this ability. The synchroextracting transform is a typical postprocessing algorithm that has the advantage of energy aggregation. However, based on a short-time Fourier transform, shortcomings such as a fixed window length and amplitude distortion when processing frequency modulation signals are unavoidable. This paper proposes a time–frequency postprocessing algorithm with high adaptability, which is called the adaptive synchroextracting transform (ASET). The filter window width for the ASET is adaptive and is determined by the instantaneous frequency change rate for the signal. On this basis, the improved extraction operator can be used to achieve a high-resolution time–frequency​ representation. This algorithm can be used to better deal with strong frequency modulation signals and has better noise robustness while allowing for signal reconstruction. The effectiveness and practicability of the proposed algorithm are demonstrated by simulation signals and faulty bearing signals. • Adaptive synchroextracting transform (ASET) can adaptively determine the window width by the frequency characteristics of the signal. • The ASET can effectively correct the amplitude and has the ability to process strong frequency modulation signals. • The ASET can effectively characterize the time-varying characteristics of the signal and has strong noise robustness. • The ASET can effectively realize signal reconstruction. [ABSTRACT FROM AUTHOR]

• EEG and eye movements were recorded concurrently during natural sentence reading. • Previously known sentences, like proverbs, are read differently than common ones. • On EEG amplitude, differences are evoked by predictability-associated variables. • On EEG time–frequency analyses, differences are observed mainly on the beta band. • Strikingly, differences are observed after the reader recognize the sentence. Predictions of incoming words performed during reading have an impact on how the reader moves their eyes and on the electrical brain potentials. Eye tracking (ET) experiments show that less predictable words are fixated for longer periods of times. Electroencephalography (EEG) experiments show that these words elicit a more negative potential around 400 ms (N400) after the word onset when reading one word at a time (foveated reading). Nevertheless, there was no N400 potential during the foveated reading of previously known sentences (memory-encoded), which suggests that the prediction of words from memory-encoded sentences is based on different mechanisms than predictions performed on common sentences. Here, we performed an ET-EEG co-registration experiment where participants read common and memory-encoded sentences. Our results show that the N400 potential disappear when the reader recognises the sentence. Furthermore, time–frequency analyses show a larger alpha lateralisation and a beta power increase for memory-encoded sentences. This suggests a more distributed attention and an active maintenance of the cognitive set, in concordance to the predictive coding framework. [ABSTRACT FROM AUTHOR]

The wheelset bearing is an indispensable part of the high-speed train, and monitoring its service performance is a concern of many researchers. Effective extraction of those impulse signals induced by the defects on the bearing elements is the key to fault detection and behaviour analysis. However, the presence of considerable noise and irrelevant components brings difficulties to extracting the wheelset bearing fault impulse signals from the measured vibration signals. This paper proposes an improved explicit shift-invariant dictionary learning (IE-SIDL) method to address this issue. Based on the shift-invariant characteristics of the wheelset bearing fault impulse signal in the time-domain, the circulant matrix is used to construct a shift-invariant dictionary and explicitly characterize the fault impulses at any time. To improve the efficiency of dictionary learning, a method of three flips is introduced to realize fast dictionary construction, and the frequency-domain reconstruction property of the circulant matrix is employed to quickly update the dictionary. Besides, an indicator-guided subspace pursuit (SP) method based on the sparsity of envelope spectrum (SES) is adopted for the sparse coding to improve sparse solution accuracy and adaptation. The effectiveness of the IE-SIDL method is proved through the simulated and experimental signals. The results demonstrate that the improved dictionary learning method has an excellent capacity in extracting fault impulse signal of the wheelset bearings, and the good time- and frequency-domain characteristics of the processed signals facilitate fault detection and behaviour analysis. • The method of three flips is employed to fast construct the explicit dictionary. • Subspace pursuit is adopted to improve the accuracy of the sparse solution. • Proposing one key parameter selection strategy for the SP method. • Good time-domain characteristics facilitate fault dynamic behaviour analysis. [ABSTRACT FROM AUTHOR]

The most important challenge for both developed and developing countries is to ensure sustainability while struggling with environmental degradation. CO 2 emissions as a proxy for environmental degradation can be considered an obstacle to sustainability. There exist several significant works in the literature on the effects of solar energy use on environmental degradation/sustainability. In this study, the effects of the use of solar energy within different time and frequency dimensions on CO 2 emissions were examined with the methodology of the continuous wavelet transform. The paper investigated the association between solar energy consumption and total energy-related CO 2 emissions in the USA through Morlet wavelet analysis, which is one of the most advanced time-frequency analysis methods for the period 1990:1–2022:6. In the wavelet coherency computations, geothermal energy consumption, hydroelectric energy consumption, industrial production, and manufacturing industry production variables were also included as control variables. Empirical findings demonstrate that solar energy consumption can have reducing effects on CO 2 emissions at lower frequencies (longer-term cycles) and sub-time periods (2014:1–2022:1) in the USA. The findings can guide the energy and environmental policies of developed and developing countries that aim to struggle with global warming and/or climate change through the increase in solar energy usage. Source: The graphical abstract is a slightly modified version of Bilgili et al. (2022), Fig. 2. [Display omitted] • Investigates the impacts of solar energy use on CO2 emissions in the USA. • Follows wavelet/partial wavelet transform models. • Employs the control variables of industrial production, geothermal and hydro energy, as well. • The reducing effect of solar energy on CO 2 emissions appears at a lower frequency. • Solar energy is not effective in the reduction of CO 2 emissions at higher frequencies. [ABSTRACT FROM AUTHOR]

A microseismic monitoring simulation experiment of coal and gas outbursts was carried out to study the microseismic response characteristics of the coal and gas outburst process. The microseismic response characteristics were analyzed from the microseismic waveform and time frequency at each stage of the outburst process. The quantitative description of the outburst process was achieved by the statistical analysis of microseismic signals and the determination of the microseismic energy index of outburst hazards. The results showed that the coal and gas outburst process included four stages: preparation, start, development, and stop, and the outburst moment is at the development stage. The protruding hole in the experiment was an ellipsoid with a small mouth and large cavity. Frequency components were complex and extensive and tended to high frequencies at the prominent stage. The frequency of the microseismic response waveform at the moment of prominence was higher than that before and after prominence, but energy at the moment of prominence was not maximum. Sudden changes in amplitudes at the preparation and development stages had more outbursts, and the maximum equivalent energy at the moment of prominence was as high as 45,000 Hz. Compared with the local high amplitude at the preparation stage, the development stage presents an overall high amplitude. Although the start stage is calmer, it is a critical period for predicting outbursts, and the microseismic activity characteristics of the outburst stage differed significantly from those of the other three stages. Quantitative indices reflecting the risk strength of coal and gas outbursts at the breeding stage were defined from microseismic energy, and the outburst risk indices under experimental conditions were established. When L 1 < 3.64, L 2 < 0.16, and L 3 < 5.74, the coal mass had a weak outburst risk; when L 1 > 3.64, L 2 > 2.33, and L 3 > 5.74, it had strong outburst risks. The research results provide an idea for coal and gas outburst precursor information and dynamic early warning. [ABSTRACT FROM AUTHOR]

Implicit learning plays an important role in the language acquisition. In addition to helping people acquire the form-level rules (e.g., the word order regularities), implicit learning can also facilitate the acquisition of word meanings (i.e., the establishment of connections between the word form and its meanings). Although some behavioral studies have explored the processing of implicit form-meaning connections, the neural dynamics underlying this processing remains unclear. Through examining whether participants could implicitly acquire the literal and metaphorical meanings of novel words, and applying the time-frequency analysis on the electroencephalogram (EEG) data collected in the testing phase, the neural oscillations corresponding to the processing of implicit form-literal and form-metaphorical meaning connections were explored. The results showed that participants in the experimental group could implicitly acquire the form-literal and form-metaphorical meaning connections after training, while participants in the control group who were not trained did not have access to such form-meaning connections. Meanwhile, during the processing of form-literal meaning connections, the greater suppression of alpha oscillations was induced by the testing items that follow the same rules as the training items (i.e., the regular testing items) in the experimental group, whereas the stronger enhancement of theta oscillations was elicited by the regular testing items in the experimental group during the processing of form-metaphorical meaning connections. Our study provides insights for understanding the processing of implicit form-literal and form-metaphorical meaning connections and the neural dynamics underlying the processing. • Participants can implicitly learn the literal and metaphorical meanings of novel characters. • The suppression of alpha oscillations is associated with the processing of implicit form-literal meaning connections. • The enhancement of theta oscillations is linked to the processing of implicit form-metaphorical meaning connections. [ABSTRACT FROM AUTHOR]

Using BK's frequency connectivity method, this paper studies the static and dynamic volatility spillovers between regional financial markets at different frequencies from 2006 to 2022, and during the Global Financial Crisis, the European Debt Crisis and the COVID-19. Our findings indicate that (1) Transnational financial stress is strongly correlated at different frequencies and volatility spillover effects are mainly driven by long-term factors and have significant time-varying characteristics with significant fluctuations during crisis events. (2) The spillover level of financial stress in the short run is similar to some extent, while the spillover level in the long run shows obvious differences. [ABSTRACT FROM AUTHOR]

Individuals with bipolar I disorder (BD) have difficulty inhibiting context-inappropriate responses. However, neural mechanisms of impaired cognitive control over impulsive behaviors, especially in response to emotion, are unclear. Theta-band neural oscillatory activity over midfrontal areas is thought to reflect cognitive control. The current study examined behavioral performance and theta-band activity during inhibition to affective stimuli in BD, relative to healthy control participants (HC). Sixty-seven participants with BD and 48 HC completed a Go/No-Go task with emotional face stimuli during electroencephalography (EEG) recording. Behavior was measured with reaction time, discriminability (d') and response bias (β). Time-frequency decomposition of EEG data was used to extract event-related theta-band (4–7 Hz) neural oscillatory power and inter-trial phase consistency (ITPC) over midline fronto-central areas. Behavior and theta-band activity were compared between groups, while covarying for age. Participants with BD exhibited slower response execution times on correct Go trials and reduced behavioral discrimination of emotional versus neutral faces, compared to HC. Theta-band power and ITPC were reduced in BD relative to HC. Theta-band power was higher on No-Go trials than Go trials. The magnitude of differences in theta-band activity between Go/No-Go trial types did not differ between groups. Increased theta-band power was associated with faster response execution times, greater discrimination of differing facial expressions, and stronger tendency to respond both across the full sample and within the BD group. Attenuated midline fronto-central theta-band activity may contribute to reduced cognitive control and maladaptive behavioral responding to emotional cues in individuals with BD. [ABSTRACT FROM AUTHOR]

Thirty percent of patients with schizophrenia do not respond to non-clozapine antipsychotics and are termed treatment-resistant schizophrenia (TRS). The 40-Hz auditory steady-state response (ASSR) is a well-known to be reduced in patients with schizophrenia compared to healthy controls (HCs), suggesting impaired gamma oscillation in schizophrenia. Given no ASSR study on TRS, we aimed to examine the neurophysiological basis of TRS employing 40-Hz ASSR paradigm. We compared ASSR measures among HCs, patients with non-TRS, and patients with TRS. TRS criteria were defined by a score of 4 or higher on two items of the Positive and Negative Syndrome Scale (PANSS) positive symptoms despite standard antipsychotic treatment. Participants were examined for ASSR with 40-Hz click-train stimulus, and then time-frequency analysis was performed to calculate evoked power and phase-locking factor (PLF) of 40-Hz ASSR. A total of 79 participants were included: 27 patients with TRS (PANSS = 92.6 ± 15.8); 27 patients with non-TRS (PANSS = 63.3 ± 14.7); and 25 HCs. Evoked power in 40-Hz ASSR was lower in the TRS group than in the HC group (F 2,79 = 8.37, p = 0.015; TRS vs. HCs: p = 0.012, d = 1.1) while no differences in PLF were found between the groups. These results suggest that glutamatergic and GABAergic neurophysiological dysfunctions are involved in the pathophysiology of TRS. Our findings warrant more comprehensive and longitudinal studies for deep phenotyping of TRS. [ABSTRACT FROM AUTHOR]

Large cement storage towers are commonly exposed to both quarry production blasting and the excitations from vibratory plant. Several seismographs were placed in the quarry to monitor blast-induced vibrations in the vicinity of the cement plant. Moreover, two ultra-sensitive vibration acquisition stations were installed respectively, at the bottom and at the top of the cement storage tower. Both stations were synchronized allowing them to be set on the same time scale. An advanced processing procedure was adopted in order to separate the effect of each excitation (i.e. blasting and vibratory machines). The wall displacements/strains were then estimated at both ends of the tower. The frequencies associated with the vibratory machines showed a noticeable dominance comparing to the waves linked to the blasting activities and eventually higher than the natural frequency of the structure. Ultra-high excitation frequencies led to the amplification of the vibrations induced from the vibratory machine for both events. Vibrations from the production shots as well as those from the vibratory machines has been found to have minor effect on the structural integrity of the tower, with induced strains fall below the critical thresholds that may cause cracking within the tower's walls. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of asynchronous fault detection filtering design for continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems in finite-frequency domain. The objective is to design an admissible piecewise affine filter such that the asymptotic stability of the filtering error system with the prescribed finite-frequency H − / H ∞ performance can be guaranteed. It is assumed that the premise variables of the plant are unmeasurable so that the filter state transition and the plant state transition may be asynchronous. By applying the celebrated S -procedure, the generalized Kalman-Yakubovič-Popov lemma is extended such that the finite-frequency H − / H ∞ performance of the fuzzy affine filtering error system is ensured. Furthermore, by utilizing piecewise quadratic Lyapunov functions, Projection lemma, and some matrix inequality linearization techniques, the finite-frequency fault detection filtering design approach is developed for the concerned T-S fuzzy affine dynamic system. It is shown that sufficient conditions for the existence of the fault detection filter are formulated as feasibility of a set of linear matrix inequalities. Finally, the effectiveness and advantages of the proposed design approach are illustrated by simulation studies. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of the evolution of international trade after COVID-19, examining commodity prices, the shipping industry, and the influence of the cost of bunker fuel. To this end, we use techniques based on fractional integration, fractional cointegration VAR (FCVAR) and wavelet analysis. Monthly data relating to heavy fuel oil prices and the shipping market from October 2011 to September 2021 are used. Using fractional integration in the post-break period, a lack of mean reversion is observed in all cases, which means that, for the commodity prices and shipping market indices, a change in trend will be permanent after COVID-19 unless strong measures are carried out by the authorities. Using wavelet analysis, we conclude that the demand shock represented in the indices mentioned above has led the price of fuel oil since the beginning of the pandemic, and bunker fuel is not relevant in determining the cost of maritime transport. [ABSTRACT FROM AUTHOR]

• Borehole wall failure and released stress waves reveal macro- and micro-scale rockburst mechanisms. • Acoustic emission time-frequency parameters reveal rock's structural changes, crack scale, and damage modes. • Hidden large-scale through-type structural planes in rock promote rockburst evolution. Deep underground projects are in a complex in-situ stress environment, and rock burst disasters induced by the hard and brittle failure of the rock mass are prone to occur around the tunnel openings. In this study, we aim to investigate the impacts of geological weak surfaces in rock masses on rockbursts and the spatiotemporal evolution of microcracks during the development of rockbursts. To achieve this objective, two sets of cubic specimens containing a circular through-hole were designed. One set of specimens had prefabricated unfilled cracks around the openings. Subsequently, the rockburst development process within the deep tunnel was replicated by subjecting both groups of specimens to identical gradient loading using a true triaxial test system. During the test, a micro camera was used to record the failure of the borehole wall in real-time, and an acoustic emission monitoring system was utilized to capture the stress waves released during structural damage. Finally, a multi-level synergistic analysis of the rockburst damage mechanism was carried out based on the macro-imaging data and micro-acoustic emission data. The results show that failure in high-stress environments within tunnels mainly includes microcrack initiation, particle ejection, crack propagation, local cracking, slabbing spalling, damage penetration, and rockburst damage. The time–frequency domain information of acoustic emission signals is highly perceptive and representative of the structural damage state of the specimens. The mutation characteristics observed in time-domain parameters, such as acoustic emission amplitude, event rate, ringing, and cumulative energy, correspond to drastic alterations in the internal structure of the rock. The distribution characteristics of frequency-domain parameters, such as acoustic emission peak frequency, dominant frequency energy, and frequency centroid, reflect different crack scales and damage modes. The diffusion of frequency centroid indicates that the damage and failure patterns within the rock are evolving towards a more complex direction. The energy magnitude of acoustic emissions represents the damage intensity during the development of rockburst. The root causes of induced structural-control type rockbursts in deep hard brittle rock masses are the combined effects of localized high-stress concentrations and large-scale discontinuous geological structures, such as natural joints, fissures, and structural planes. The naturally occurring large-scale through-type geological weak surfaces within the rock mass reduce the strength of the surrounding rock, alter the location of stress concentration, and change the initial damage characteristics. Moreover, they promote the evolution of rockbursts, thereby increasing their destructive intensity. [ABSTRACT FROM AUTHOR]

• The settlement threshold of bridge pier for traffic safety at different speeds is determined. • The beveled settlement threshold of subgrade for driving safety under different speed and different length of transition zone is determined. • Time-frequency analysis is used to reveal the dynamic response characteristics of pier settlement and bending deformation of subgrade in frequency domain. The settlement of piers and subgrade bending deformation are widely recognized as common issues in the transition zones of high-speed railway bridges. This study aims to investigate the settlement behavior within these transition zones and its impact on the dynamic interaction between trains and the track. To achieve this, a vehicle-track-transition zone mapping relationship model is developed to analyze both the settlement behavior and the resulting dynamic response characteristics. The study employs the finite element method and multi-body dynamics to construct the simulation model. Settlement effects are simulated using the Newton-Raphson iterative method, with the additional rail deformation caused by foundation settlement serving as the excitation for the vehicle-track-transition zone dynamic interaction system. In the numerical analysis, the dynamic effects of three key factors—train speed, transition zone length, and the amplitude of foundation settlement—are examined based on the performance of the vehicle-track-transition zone interaction. The time-frequency technique is utilized to comprehensively reveal and clarify the spatial-frequency characteristics of system responses influenced by settlement excitation. Moreover, the relationship between the safety-based settlement threshold and these three factors is calibrated. [ABSTRACT FROM AUTHOR]

• The multi-axis load spectrum editing method of automotive components is studied. • A time–frequency editing method based on the generalized S-transform is proposed. • The fatigue damage of the lower swing arm bushing is analyzed numerically. Fatigue bench testing efficiency for automotive components can be significantly enhanced through load spectrum editing. However, current methodologies for editing uni-axial load spectra often overlook inherent phase correlations in the loading process, potentially leading to inaccurate test results. To address this issue, a novel time–frequency editing approach for multi-axis load spectra of automotive components has been introduced, utilizing the generalized S-transform (GST) theory. This approach identifies and eliminates load components with minimal impact on fatigue damage, generating an edited spectrum with a condensed duration without compromising the integrity of the original data. To evaluate the editing impact, the multi-axis load spectra were subjected to three distinct editing techniques: the time domain damage retention method, the wavelet transform method, and the GST-based method. Comparative analysis revealed that the GST-based method not only significantly reduced the load spectrum's duration but also preserved the original spectrum's phase relationships, statistical characteristics, power spectral density, and level counting. The efficacy of the GST-based editing technique was further validated through bushing fatigue simulations, confirming its superiority in enhancing the efficiency of automotive component fatigue testing. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic characteristics (natural frequencies and damping ratios) of a 600-m-tall supertall building with an active tuned mass damper (ATMD) system during Super Typhoon Saola. Based on the wind speed, wind direction, and wind-induced acceleration response records by the structural health monitoring (SHM) system installed on the skyscraper, the wind characteristics during the passage of Saola are first investigated, and the relations between the mean wind speeds atop the skyscraper and the root-mean-square (RMS) acceleration responses are established. Then, employing the stochastic subspace identification (SSI) method and an uncertainty quantification method, this paper comprehensively investigates the uncertainty bounds, time-varying features, and amplitude-dependency characteristics of the modal parameters with and without the operation of the ATMD system. Next, on the basis of the identified modal parameters and time-frequency analysis, the performance and effectiveness of the ATMD system in suppressing the wind-induced vibrations of the skyscraper is investigated. Lastly, the structural health condition is evaluated from the perspective of building natural frequency and damping ratio changes before and after the typhoon hit. The goal of this study is to advance the understanding of the wind effects on supertall buildings and provide useful information for the wind-resistant design and vibration control of supertall buildings in tropical cyclone-prone regions. • Conduct field measurements on a 600-m-high skyscraper with an active tuned mass damper system during Super Typhoon Saola. • Investigate uncertainty bounds, time-varying features, and amplitude-dependency of modal parameters of the skyscraper. • Assess performance and effectiveness of the ATMD in suppressing wind-induced vibrations of the skyscraper. • Evaluate structural health condition based on building frequency and damping ratio changes before and after typhoon hit. [ABSTRACT FROM AUTHOR]

Time series data pervades multiple domains, yet in real-world applications, there frequently exists a deficiency of labels to ascertain effective representations and enable efficient classification. In this work, we introduce SMDE, a unique time series contrastive learning framework, diverging from conventional techniques that predominantly concentrate on instance-level contrast. SMDE leverages signal decomposition and ensemble methodologies, enhancing our focus on localized mode alterations, within time series signals. We further introduce the global signal consistency and intrinsic mode consistency mechanisms to promote the robust learning of global and local attributes. To ensure more efficient convergence, we present the DE Circle loss furnished with an adaptive weighting mechanism, founded on global signal consistency and intrinsic mode consistency principles. The SMDE framework is designed to be applicable to both univariate and multivariate time series analysis, indicating its potential for a wide range of temporal data applications. Experiments on 128 univariate UCR datasets reveal that SMDE improves the average accuracy by 4.73% over existing techniques. Additionally, our experiments on 29 multivariate UEA datasets demonstrate an average accuracy improvement of 7.42% compared to current methodologies. Moreover, through extensive semi-supervised experimentation, SMDE demonstrates superior performance in 15 disparate semi-supervised configurations, attaining an equivalent performance level to supervised training with 100% labeled data, even with a minimal portion of the labels. The code is publicly available at https://github.com/haobinlaosi/smde. [ABSTRACT FROM AUTHOR]

In contemporary times, where most academic research mainly focuses on the factors of economic and environmental sustainability and emissions reduction. Yet, very little attention has been paid to the identification of the factors of renewable energy, which requires appropriate policy-level attention. Consequently, this research investigated two developed economies, i.e., Germany and the USA, from 1991 to 2021 where, the objective of the study includes using novel and robust empirical methods to test the causal relationship between renewable energy and CO2 emissions, economic growth, technological innovations and oil prices. Using the normality and unit root estimators, this study observed that non-normal data distribution, yet all the variables are stationary. Using time-series and panel cointegration tests, the results validate the cointegration between economic growth, oil prices, carbon emission, technological innovation, and renewable energy adoption in the United States whereas Germany does not show cointegration between the variables. This study employ 's the Morlet-Wavelet approaches and key findings show that all these variables have a significant role in improving renewable energy adoption in both the region. Furthermore, results show a unidirectional and bidirectional causal association between the variables via the panel-stacked Granger causality test. This study recommends effective policy ramifications concerning improved investment in technological innovation, improved low-carbon production, and diverting economic growth to renewable energy transition. Use of improved new time-series method of wavelet coherence show the key contribution in this paper with new evidence of time frequency analysis on how external variables affect renewable energy consumption in developed countries of US and Germany. The objective includes understanding the effects of CO2 emissions, economic growth, technological innovations and oil prices on renewable energy which would give evidence to policy makers and environmentalists on how developed countries should improve clean energy adoption. [ABSTRACT FROM AUTHOR]

In this paper, a horizontal rotor with passive anisotropic asymmetric magnetic bearings nonlinear model is developed. The model is based on the experimental evidence of an educational demonstrator, a powerful benchmark which highlights features of rotordynamics systems. An experimental setup is developed using laser sensors to track the displacements of the rotor and tachometer to record the rotor angular speed. Several test campaigns are performed with different initial conditions to characterise the educational demonstrator. The first test campaign is oriented to identify the system structural properties, while the second one is focused on the nonlinearity identification. The rotor modelling is divided into three stages with progressive increase in complexity, starting from the overall linear structural behaviour to the nonlinear subharmonic resonance phenomenon. The model is developed with a generalised approach suitable for a wide range of operating conditions and rotor configurations. Progressively, the developed rotor models are compared to highlight their limitations and advantages. Finally, rotor trajectory analysis and time–frequency analysis are used in the numerical-to-experimental comparison. • Rotor model with nonlinear asymmetric anisotropic supports. • Magnetic interaction surfaces for generalised restoring forces. • Model updating through experimental outcomes. • Ovoid orbit trajectories investigation at resonance regimes. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new cognitive vection paradigm was introduced without any visual, somatosensory, and auditory stimuli. • Alpha band EEG is linked to bottom-up vestibular sensory activation. • Theta band EEG is linked to self-motion perception per se. • Beta band EEG changes represent the process of updating of the prior brain state. Peripheral vestibular activation results in multi-level responses, from brainstem-mediated reflexes (e.g. vestibular ocular reflex – VOR) to perception of self-motion. While VOR responses indicate preserved vestibular peripheral and brainstem functioning, there are no automated measures of vestibular perception of self-motion – important since some patients with brain disconnection syndromes manifest a vestibular agnosia (intact VOR but impaired self-motion perception). Electroencephalography ('EEG') – may provide a surrogate marker of vestibular perception of self-motion. A related objective is obtaining an EEG marker of vestibular sensory signal processing, distinct from vestibular-motion perception. We performed a pilot study comparing EEG responses in the dark when healthy participants sat in a vibrationless computer-controlled motorised rotating chair moving at near threshold of self-motion perception, versus a second situation in which subjects sat in the chair at rest in the dark who could be induced (or not) into falsely perceiving self-motion. In both conditions subjects could perceive self-motion perception, but in the second there was no bottom-up reflex-brainstem activation. Time-frequency analyses showed: (i) alpha frequency band activity is linked to vestibular sensory-signal activation; and (ii) theta band activity is a marker of vestibular-mediated self-motion perception. Consistent with emerging animal data, our findings support the role of theta activity in the processing of self-motion perception. [ABSTRACT FROM AUTHOR]

Deep learning methods, particularly convolutional neural networks (CNNs), have advanced the field of motor imagery (MI) by effectively extracting spatio-spectral–temporal (SST) features from electroencephalography (EEG) signals. Image-based CNNs using time–frequency analysis such as wavelet transform have outperformed signal-based CNNs in MI-EEG classification. However, the spatial characteristics of EEG signals can be neglected due to the high dimensionality of spectral–temporal feature space. Moreover, the performance hinges on whether the predefined transformation can capture the class-discriminative spectral–temporal representations of EEG signals well. Despite attempts to alleviate these challenges by selecting EEG electrodes or restricting spectral–temporal feature space, large inter-subject variabilities have impeded successful MI-EEG decoding. In this paper, we propose a learnable continuous wavelet-based multi-branch attentive CNN framework for decoding MI-EEG signals. Our method is designed to automatically generate class-discriminative SST representations of EEG signals within subject-specific sub-bands by utilizing learnable wavelet-based convolutions. Additionally, it employs multi-branch attentive CNNs for the effective and efficient extraction of local and global SST features. Our comprehensive evaluation on two public datasets demonstrates that the proposed method significantly outperformed state-of-the-art methods. We also conduct an ablation study under various configuration settings within the proposed framework to demonstrate the effectiveness of our method. The proposed method provides a powerful and innovative approach to MI-EEG signal decoding, with implications for personalized EEG decoding. • Motor Imagery-EEG decoding plays a critical role in neurorehabilitation and BCI. • We introduce a novel method for spatio-spectral–temporal motor imagery-EEG decoding. • Our method transforms EEG into discriminative SST representations automatically. • Our multi-branch attentive CNN learns local/global SST features simultaneously. • We demonstrate its effectiveness on public datasets by comparing with SOTA methods. [ABSTRACT FROM AUTHOR]

• The influences of pore pressure and in situ stress are simultaneously considered in in situ environments at different depths. • The influences of both the deep in situ environment and the engineering disturbance are considered to reflect the mechanical properties and response patterns of rock undergoing disturbance-induced disaster in a deep in situ environment. • The marble damage and failure mechanisms are comprehensively elucidated based on AE time–frequency characteristics. The safety of the surrounding rock in deep underground engineering is crucial. To understand the surrounding rock hazards at different depths of the Jinping underground cavern, triaxial compression tests were conducted, and the acoustic emission (AE) of Jinping marble samples was monitored while considering the influence of engineering disturbances and in situ stress conditions at different burial depths (100 m, 1000 m, 1400 m, 1800 m, and 2400 m). The results revealed that the AE events were dominated by small and medium-sized energy releases of magnitude 0–2, accounting for approximately 90 % of all AE events. The dominant frequency range of the microfracture sources was 0–450 kHz. As the burial depth increases, the AE frequency band aggregation time tended to stabilise at the average frequency during the loading process, and the failure mode trended towards ductile failure. This study offers theoretical support for assessing the operational security of deep underground caverns. [ABSTRACT FROM AUTHOR]

• A novel method of shearer cutting state recognition based on traction resistance and traction speed is proposed. • The factors affecting the shearer's cutting state include coal-rock strata conditions and operational parameters. • Time and frequency domain analyses of shearer traction resistance determine the important and sensitive features. • Simulation and experimental analysis proved the effectivity and superiority of the proposed method. Accurate identification of the shearer cutting state is essential for the intelligent control of the shearer. In addition to the coal-rock strata conditions, the operational parameters of the shearer also influence the shearer cutting state. To analyze the factors on the shearer cutting state and accurately identify its cutting state, the characteristics of shearer traction resistance are analyzed under various working conditions using discrete element numerical simulation. A method of the shearer cutting state recognition based on traction resistance and traction speed is proposed. Firstly, the mechanical properties of coal and rock are obtained through uniaxial compression experiments. The joint simulation model is developed to analyze the change in traction resistance under different traction speeds. The functional relationship between traction resistance and traction speed is obtained. Then, the influence of gangue thickness and position on traction resistance is analyzed. The time domain and frequency domain analysis of traction resistance is conducted to more accurately describe the characteristics of shearer traction resistance. Finally, simulation and experimental data are used to verify the reliability of the proposed method. The recognition accuracy of simulation and experimentation reaches 100 % and 97.1 %, respectively. This indicates that the proposed method can effectively recognize the shearer cutting state. [ABSTRACT FROM AUTHOR]

• Cortical Auditory Evoked Potentials (CAEPs) measured in normal hearing (NH) listeners and bimodal cochlear implant (CI) users. • Time-frequency analysis of CAEPs as a tool to investigate integration. • Interaural temporal mismatch resulted in reduced N1 amplitude and phase locking value in NH listeners. • CAEPs offer a means to estimate cortical delay between electric and acoustic stimulation in bimodal individuals. • Correcting individual temporal interaural mismatch in bimodal CI users led to an increase in N1 amplitude. • Limited phase locking increase suggests constrained electric-acoustic cortical integration. Combining a cochlear implant with contralateral acoustic hearing typically enhances speech understanding, although this improvement varies among CI users and can lead to an interference effect. This variability may be associated with the effectiveness of the integration between electric and acoustic stimulation, which might be affected by the temporal mismatch between the two listening sides. Finding methods to compensate for the temporal mismatch might contribute to the optimal adjustment of bimodal devices and to improve hearing in CI users with contralateral acoustic hearing. The current study investigates cortical auditory evoked potentials (CAEPs) in normal hearing listeners (NH) and CI users with contralateral acoustic hearing. In NH, the amplitude of the N1 peak and the maximum phase locking value (PLV) were analyzed under monaural, binaural, and binaural temporally mismatched conditions. In CI users, CAEPs were measured when listening with CI only (CIS_only), acoustically only (AS_only) and with both sides together (CIS+AS). When listening with CIS+AS, various interaural delays were introduced between the electric and acoustic stimuli. In NH listeners, interaural temporal mismatch resulted in decreased N1 amplitude and PLV. Moreover, PLV is suggested as a more sensitive measure to investigate the integration of information between the two listening sides. CI users showed varied N1 latencies between the AS_only and CIS_only listening conditions, with increased N1 amplitude when the temporal mismatch was compensated. A tendency towards increased PLV was also observed, however, to a lesser extent than in NH listeners, suggesting a limited integration between electric and acoustic stimulation. This work highlights the potential of CAEPs measurement to investigate cortical processing of the information between two listening sides in NH and bimodal CI users. [ABSTRACT FROM AUTHOR]

As a prevalent system in radar technology, the Frequency modulated continuous wave (FMCW) radar faces significant challenges in interference. This paper proposes a three-step interference mitigation strategy considering the time–frequency (TF) features of interference and time-domain properties of beat signals. Initially, the process involves statistical analysis of chirp rates from high-energy TF points, estimating the matched chirp rates of interference signals. Subsequently, the improved multisynchrosqueezing chirplet transform (IMSSCPT) is suggested for high-resolution TF characterization of interference signals. Utilizing the ridge extraction method, we accurately detect the TF region of interference signals. This precise localization enables the gradual separation of the reconstructed interference from the received signals. Finally, we perform parallel singular value decomposition on Hankel matrices, which are stacked from the mitigated subsequences to obtain the beat subspace and reconstruct the beat signal to reduce the negative effect of noise. A series of experimental comparisons of the proposed algorithm with existing methods have validated its exceptional performance, particularly under conditions of high-level noise. • Employ statistical analysis to estimate matched chirp rates of interference signals. • Introduce IMSSCPT to enhance TF distribution of interference signals. • IMSSCPT-based mask method effectively mitigates strong interferences. • Subspace denoising reliably removes noise and recovers target signals. [ABSTRACT FROM AUTHOR]