To address rotor imbalance and misalignment in oil transfer pumps, an innovative diagnostic framework using Residual Network (ResNet) is proposed. The model incorporates advanced signal processing algorithms and strategic sensor placement to enhance diagnostic efficacy. A fault simulation test rig captured vibration signals from eight key measurement points on the pump. One-dimensional and multi-dimensional signal processing techniques generated comprehensive datasets for training and validating the model. Sensor placement optimization, focusing on the bearing seat\'s axial direction, inlet flange\'s vertical direction, and outlet flange\'s axial direction, increased rotor fault sensitivity. Time-frequency data processed via Short-Time Fourier Transform (STFT) achieved the highest diagnostic accuracy, surpassing 98 %. This study highlights the importance of optimal signal processing and precise sensor placement in improving the accuracy of diagnosing rotor faults in oil transfer pumps, thus enhancing the operational reliability and efficiency of energy transportation systems.

Time-Frequency (T-F) analysis of EEG is a common technique to characterise spectral changes in neural activity. This study explores the limitations of utilizing conventional spectral techniques in examining cyclic event-related cortical activities due to challenges, including high inter-trial variability. Introducing the Cycle-Frequency (C-F) analysis, we aim to enhance the evaluation of cycle-locked respiratory events. For synthetic EEG that mimicked cycle-locked pre-motor activity, C-F had more accurate frequency and time localization compared to conventional T-F analysis, even for a significantly reduced number of trials and a variability of breathing rhythm. Preliminary validations using real EEG data during both unloaded breathing and loaded breathing (that evokes pre-motor activity) suggest potential benefits of using the C-F method, particularly in normalizing time units to cyclic activity phases and refining baseline placement and duration. The proposed approach could provide new insights for the study of rhythmic neural activities, complementing T-F analysis. .

Neurophysiological brain activity comprises rhythmic (periodic) and arrhythmic (aperiodic) signal elements, which are increasingly studied in relation to behavioral traits and clinical symptoms. Current methods for spectral parameterization of neural recordings rely on user-dependent parameter selection, which challenges the replicability and robustness of findings. Here, we introduce a principled approach to model selection, relying on Bayesian information criterion, for static and time-resolved spectral parameterization of neurophysiological data. We present extensive tests of the approach with ground-truth and empirical magnetoencephalography recordings. Data-driven model selection enhances both the specificity and sensitivity of spectral and spectrogram decompositions, even in non-stationary contexts. Overall, the proposed spectral decomposition with data-driven model selection minimizes the reliance on user expertise and subjective choices, enabling more robust, reproducible, and interpretable research findings.

BACKGROUND: 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.
OBJECTIVE: 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?
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.

BACKGROUND: Depression is usually characterized by impairments in reward function, and shows altered motivation to reward in reinforcement learning. This study further explored whether task difficulty affects reinforcement learning in college students with and without depression symptom.
METHODS: The depression symptom group (20) and the no depression symptom group (26) completed a probabilistic reward learning task with low, medium, and high difficulty levels, in which task the response bias to reward and the discriminability of reward were analyzed. Additionally, electrophysiological responses to reward and loss feedback were recorded and analyzed while they performed a simple gambling task.
RESULTS: The depression symptom group showed more response bias to reward than the no depression symptom group when the task was easy and then exhibited more quickly decrease in response bias to reward as task difficulty increased. The no depression symptom group showed a decrease in response bias only in the high-difficulty condition. Further regression analyses showed that, the Feedback-related negativity (FRN) and theta oscillation could predict response bias change in the low-difficulty condition, the FRN and oscillations of theta and delta could predict response bias change in the medium and high-difficulty conditions.
CONCLUSIONS: The electrophysiological responses to loss and reward were not recorded in the same task as the reinforcement learning behaviors.
CONCLUSIONS: College students with depression symptom are more sensitive to task difficulty during reinforcement learning. The FRN, and oscillations of theta and delta could predict reward leaning behavior.

Human Activity Recognition (HAR), alongside Ambient Assisted Living (AAL), are integral components of smart homes, sports, surveillance, and investigation activities. To recognize daily activities, researchers are focusing on lightweight, cost-effective, wearable sensor-based technologies as traditional vision-based technologies lack elderly privacy, a fundamental right of every human. However, it is challenging to extract potential features from 1D multi-sensor data. Thus, this research focuses on extracting distinguishable patterns and deep features from spectral images by time-frequency-domain analysis of 1D multi-sensor data. Wearable sensor data, particularly accelerator and gyroscope data, act as input signals of different daily activities, and provide potential information using time-frequency analysis. This potential time series information is mapped into spectral images through a process called use of \'scalograms\', derived from the continuous wavelet transform. The deep activity features are extracted from the activity image using deep learning models such as CNN, MobileNetV3, ResNet, and GoogleNet and subsequently classified using a conventional classifier. To validate the proposed model, SisFall and PAMAP2 benchmark datasets are used. Based on the experimental results, this proposed model shows the optimal performance for activity recognition obtaining an accuracy of 98.4% for SisFall and 98.1% for PAMAP2, using Morlet as the mother wavelet with ResNet-101 and a softmax classifier, and outperforms state-of-the-art algorithms.

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.

Joint attention is an indispensable tool for daily communication. Abnormalities in joint attention may be a key reason underlying social impairment in schizophrenia spectrum disorders. In this study, we aimed to explore the attentional orientation mechanism related to schizotypal traits in a social situation. Here, we employed a Posner cueing paradigm with social attentional cues. Subjects needed to detect the location of a target that is cued by gaze and head orientation. The power in the theta frequency band was used to examine the attentional process in the schizophrenia spectrum. There were four main findings. First, a significant association was found between schizotypal traits and attention orientation in response to invalid gaze cues. Second, individuals with schizotypal traits exhibited significant activation of neural oscillations and synchrony in the theta band, which correlated with their schizotypal tendencies. Third, neural oscillations and synchrony demonstrated a synergistic effect during social tasks, particularly when processing gaze cues. Finally, the relationship between schizotypal traits and attention orientation was mediated by neural oscillations and synchrony in the theta frequency band. These findings deepen our understanding of the impact of theta activity in schizotypal traits on joint attention and offer new insights for future intervention strategies.

Bridges may undergo structural vibration responses when exposed to seismic waves. An analysis of structural vibration characteristics is essential for evaluating the safety and stability of a bridge. In this paper, a signal time-frequency feature extraction method (NTFT-ESVD) integrating standard time-frequency transformation, singular value decomposition, and information entropy is proposed to analyze the vibration characteristics of structures under seismic excitation. First, the experiment simulates the response signal of the structure when exposed to seismic waves. The results of the time-frequency analysis indicate a maximum relative error of only 1% in frequency detection, and the maximum relative errors in amplitude and time parameters are 5.9% and 6%, respectively. These simulation results demonstrate the reliability of the NTFT-ESVD method in extracting the time-frequency characteristics of the signal and its suitability for analyzing the seismic response of the structure. Then, a real seismic wave event of the Su-Tong Yangtze River Bridge during the Hengchun earthquake in Taiwan (2006) is analyzed. The results show that the seismic waves only have a short-term impact on the bridge, with the maximum amplitude of the vibration response no greater than 1 cm, and the maximum vibration frequency no greater than 0.2 Hz in the three-dimensional direction, indicating that the earthquake in Hengchun will not have any serious impact on the stability and security of the Su-Tong Yangtze River Bridge. Additionally, the reliability of determining the arrival time of seismic waves by extracting the time-frequency information from structural vibration response signals is validated by comparing it with results from seismic stations (SSE/WHN/QZN) at similar epicenter distances published by the USGS. The results of the case study show that the combination of dynamic GNSS monitoring technology and time-frequency analysis can be used to analyze the impact of seismic waves on the bridge, which is of great help to the manager in assessing structural seismic damage.

Utilizing the high temporal resolution of event-related potentials (ERPs), we compared the time course of processing incongruent color versus 3D-depth information. Participants were asked to judge whether the food color (color condition) or 3D structure (3D-depth condition) was congruent or incongruent with their previous knowledge and experience. The behavioral results showed that the reaction times in the congruent 3D-depth condition were slower than those in the congruent color condition. The reaction times in the incongruent 3D-depth condition were slower than those in the incongruent color condition. The ERP results showed that incongruent color stimuli induced a larger N270, larger P300, and smaller N400 components in the fronto-central region than the congruent color stimuli. Incongruent 3D-depth stimuli induced a smaller N1 in the occipital region, larger P300 and smaller N400 in the parietal-occipital region than congruent 3D-depth stimuli. The time-frequency analysis found that incongruent color stimuli induced a larger theta band (360-580 ms) activation in the fronto-central region than congruent color stimuli. Incongruent 3D-depth stimuli induced larger alpha and beta bands (240-350 ms) activation in the parietal region than congruent 3D-depth stimuli. Our results suggest that the human brain deals with violating general color or depth knowledge in different time courses. We speculate that the depth perception conflict was dominated by solving the problem with visual processing, whereas the color perception conflict was dominated by solving the problem with semantic violation.