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The detection and identification of epileptic seizures attracted considerable relevance for the neurophysiologists. In order to accomplish the detection of epileptic seizures or equivalently ictal EEG states, this paper proposes the use of nonlinear and chaos features not computed over the raw EEG signals as it was commonly experienced, but instead over intrinsic mode functions (IMFs) extracted subsequently to the application of newly time-frequency signal decomposition methods on the basis of empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. The first step within the proposed methodology is to excerpt the various components of the IMFs by EMD and VMD decomposition methods on time EEG segments. The Hjorth parameters, the Hurst exponent, the Recurrence Quantification Analysis (RQA), the detrended fluctuation analysis (DFA), the Largest Lyapunov Exponent (LLE), The Higuchi and Katz fractal dimensions (HFD and KFD), seven nonlinear and chaos features computed over the IMFs were investigated and their classification performances evaluated using the k-nearest neighbor (KNN) and the multilayer perceptron neural network (MLPNN) classifiers. Furthermore, the combination of the best nonlinear features has also been examined in terms of sensitivity, specificity and overall classification accuracy. The publicly available Bonn EEG dataset has been has been employed to validate the efficiency of the proposed method for detecting ictal EEG signals from normal or interictal EEG segments. Among the several experiments involved in the current study, the ultimate results establish that the overall classification accuracy can achieve 100%, 99.45%, 99.8%, 99.8%, 98.6% and 99.1% for six different epileptic seizure detection case problems studied, confirming the ability of the proposed methodology in helping the clinic practitioners in the epilepsy detection care units to classify seizure events with a great confidence. [ABSTRACT FROM AUTHOR]

Recognizing brain activity from EEG waves is an important field of study in biomedical engineering and neuroscience. Conventional approaches usually begin with signal processing techniques to extract features from the EEG data, and then machine learning algorithms are applied to classify the data. However, the spatial resolution of these EEG signals is low, which makes it difficult to pinpoint the exact location of the neural activity source in the brain. There are ongoing initiatives to use DL‐based brain activity recognition algorithms to overcome these constraints. Therefore, this work presents a novel hybrid framework for brain activity detection using the enhanced Stockwell transform and an EEG signal that is called LinkNet and modified bidirectional–long short‐term memory (LN‐MBi‐LSTM) model. This framework follows a methodical approach that includes stages for feature extraction, brain activity recognition and preprocessing. Firstly, the improved Weiner filtering (IWF) approach is used to preprocess the EEG input signal. The relevant features are then extracted using a feature extraction technique from the preprocessed EEG signal. To identify the brain activity, these recovered feature sets are subsequently processed separately using LinkNet and modified bidirectional–long short‐term memory (MBi‐LSTM). A thorough analysis that takes into account both simulation and experimental calculations is part of the validation process for the LN‐MBi‐LSTM model. Finally, this study demonstrates the therapeutic potential of the LN‐MBi‐LSTM framework by presenting a strong and verified model for brain activity recognition. With the highest precision of 0.997, the LinkNet‐MBi‐LSTM model distinguishes itself from other models and confirms its exceptional capacity to produce accurate positive predictions. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to develop innovative methods for efficient condition monitoring of electro-motors. The study investigates the current signals of electro-motors in their early stages of failure, specifically identifying high-frequency fluctuations. These fluctuations, due to their low intensity and lack of modulation, elude detection through FFT analysis. As such, this study analyzes the quantitative and qualitative nature of such disturbances, including their frequency content and intensity in healthy and defective states, and identifies and isolates high-frequency disturbances from the current signal using the EMD and CEEMDAN methods. The research involved a comprehensive analysis of the FFT spectrum, envelope, and factors of main current signals and IMFs. The results indicate mode mixing only in the defective state. The EMD results demonstrate that the IMF associated with the original signal in the defective state exhibited a frequency content of less than 50, as evidenced by the FFT and the envelope spectrums. Additionally, the amplitude of the dominant frequencies in the FFT spectrum of the disturbances was significantly higher in the defective state. Additionally, the outcomes disclose that the original signal's RMS to the disturbances' RMS was significantly higher in healthy cases. Furthermore, the crest factor ratio in the healthy state was slightly higher than that of the defective state. The CEEMDAN findings reveal that the IMFs associated with the disturbance in a healthy state exhibit characteristics akin to white noise. Conversely, some IMFs demonstrate dominant frequencies of 50 and 150 Hz in the defective state. Furthermore, the RMS ratio of the original signal to the RMS of the disturbance is higher in the healthy state than in the defective state. Additionally, the shape factor of disturbances in the healthy state surpasses that of the faulty state. However, the ratio of shape factor notably exceeds in the healthy state. [ABSTRACT FROM AUTHOR]

Actual acquired air quality time series data are highly volatile and nonstationary, and accurately predicting nonlinear time series data containing complex noise is an ongoing challenge. This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, 2015–0:00 pm on July 1, 2020). The AQI sequence is first decomposed into intrinsic mode functions (IMFs) via EMD and subsequently predicted separately via the improved transformer algorithm based on BiLSTM, where linear prediction is performed for IMFs with simple trends. Finally, the predicted values of each IMF are integrated using BiLSTM to obtain the predicted AQI values. This paper predicts the AQI in Patna with a time window of 5 h, and the RMSE, MAE and MAPE are as low as 5.6853, 2.8230 and 2.23%, respectively. Moreover, the scalability of the proposed model is validated on air quality datasets from several other cities, and the results prove that the proposed hybrid model has high performance and broad application prospects in real-time air quality prediction. [ABSTRACT FROM AUTHOR]

Evidence suggests that human emotions can be detected using Electroencephalography (EEG) brain signals. Recorded EEG signals, due to their large size, may not initially perform well in classification. For this reason, various feature selection methods are used to improve the performance of classification. The nature of EEG signals is complex and unstable. This article uses the Empirical Mode Decomposition (EMD) method, which is one of the most successful methods in analyzing these signals in recent years. In the proposed model, first, the EEG signals are decomposed using EMD into the number of Intrinsic Mode Functions (IMF), and then, the statistical properties of the IMFs are extracted. To improve the performance of the proposed model, using the RBF kernel and Least Absolute Shrinkage and Selection Operator (LASSO) feature selection, an effective subset of the features that have changed the space is selected. The data are then clustered, and finally, each cluster is classified with a decision tree and random forest and KNN. The purpose of clustering is to increase the accuracy of the classification, which is achieved by focusing each cluster on a limited number of classes. This experiment was performed on the DEAP dataset. The results show that the proposed model with 99.17% accuracy could perform better than recent research such as deep learning and show good performance. In the latest years, with the development of the BCI system, the demand for recognizing emotions based on EEG has increased. We provide a method for classifying clustered data that is efficient for high accuracy. [ABSTRACT FROM AUTHOR]

This paper presents the development of a cone-shaped radial magnetic-field-detector–based remote field eddy current (RFEC) probe for enhanced detection of localized defects in ferritic steel tubes. The performance of the radial coil detector has been evaluated for the detection of different types of defects such as through holes, flat bottom holes and notches of different sizes and orientations. The RFEC signals are observed to be corrupted by heavy noise due to spatial variations in material permeability. To remove the random noise from the nonstationary time series RFEC signals, empirical mode decomposition (EMD) processing has been adopted and has been found to significantly improve the signal-to-noise (SNR) ratio. Furthermore, the effect of the circumferential extent of defects on the signal amplitude is analyzed through experimental and finite-element-model–based studies. The linear resolution of the designed radial coil detector and its repeatability behavior are also scrutinized. Studies demonstrate that the developed radial field detector is a promising configuration in remote field eddy current technique for enhanced defect detection and also in designing an array probe. [ABSTRACT FROM AUTHOR]

Abstract Actual acquired air quality time series data are highly volatile and nonstationary, and accurately predicting nonlinear time series data containing complex noise is an ongoing challenge. This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, 2015–0:00 pm on July 1, 2020). The AQI sequence is first decomposed into intrinsic mode functions (IMFs) via EMD and subsequently predicted separately via the improved transformer algorithm based on BiLSTM, where linear prediction is performed for IMFs with simple trends. Finally, the predicted values of each IMF are integrated using BiLSTM to obtain the predicted AQI values. This paper predicts the AQI in Patna with a time window of 5 h, and the RMSE, MAE and MAPE are as low as 5.6853, 2.8230 and 2.23%, respectively. Moreover, the scalability of the proposed model is validated on air quality datasets from several other cities, and the results prove that the proposed hybrid model has high performance and broad application prospects in real-time air quality prediction.

This systematic review evaluated the effectiveness of combining enamel matrix derivative (EMD) with various bone grafts in periodontal regenerative surgery, specifically targeting intrabony defects. Randomized controlled trials with 12-month follow-ups were included. Clinical outcomes assessed included clinical attachment level, probing depth, gingival recession, and radiographic defect fill. Meta-analysis showed that adding bone grafts to EMD does not provide additional benefits in periodontal tissue examination, with improvement observed only in radiographic defect fill. Subgroup analyses examined the impact of different bone graft types, revealing that alloplastic bone grafts are effective in radiographic defect fill. The risk of bias assessment indicated a moderate risk across studies, with challenges in blinding owing to the nature of the surgical treatment. Furthermore, a minimal intervention surgical approach may not require additional bone grafts for optimal periodontal regeneration. These findings contribute to the ongoing dialogue in the field and guide clinicians toward evidence-based decisions for optimal periodontal outcomes, emphasizing the judicious use of bone grafts.

As a critical equipment in nuclear power plants, valve leakage would have an impact on system safety and stable operation. Using acoustic emission technology to identify valve leakage and distinguish between different leakage modes of internal and external leakage is helpful for targeted maintenance in the future, which is of great significance for improving maintenance economy. Valves are key equipments in nuclear power, and there are two main leakage modes. One mode is external leakage, which means that the fluid in the pipeline flows from the inside of the valve to the outside through the flange joint surface or the gap between the valve stem and the packing. Another mode is internal leakage, which refers to the leakage at the sealing surface of the valve disc inside the valve body due to poor closure or structural defects in the valve disc itself. In the case of internal leakage, the fluid in the pipeline cannot be completely cut off. The application of acoustic emission technology can achieve the identification of valve leakage, but it can only identify whether the valve has leaked and cannot identify whether the valve has leaked internally or externally. Distinguishing different leakage modes is of great significance for the subsequent development of maintenance plans. A valve leakage mode recognition method based on empirical mode decomposition (EMD) and random forest was proposed in this paper. Firstly, the leakage acoustic emission signal was decomposed by EMD to obtain component signals in different frequency bands. Secondly, Fourier transform was performed on each component signal to obtain its spectrum, and spectrum energy ratio features were extracted from the spectrum. Due to the wide frequency distribution range of the leakage acoustic emission signal, the number of features is relatively large. Therefore, it is difficult for general logic algorithms to accurately identify the leakage mode. Machine learning algorithms are very suitable for processing multi- dimensional data, so it can be used to classify valve internal and external leakage. Random forest is one of machine learning algorithms that uses decision trees as classifiers, which has significant advantages in processing data with high feature dimensions. Finally, an intelligent recognition model was established based on the random forest algorithm to achieve automatic identification of internal and external leakage. The method was validated using experimental data, and the results show that multiple component signals are obtained by decomposing the valve leakage acoustic emission signal using EMD, which refines the spectrum differences of the acoustic emission signal under different leakage modes. The spectrum energy ratio of each component signal was used as a feature for valve leakage mode recognition, achieving accurate characterization of valve internal and external leakage. A leakage mode recognition model for electric valves based on random forest was established, achieving accurate identification of different leakage modes.

This study investigates the dynamics of daily Urmia Lake level (ULL) changes using spectral analysis tools to discover fluctuating patterns in the ULL series. Therefore, in the present research, the empirical mode decomposition (EMD), variational mode decomposition (VMD), empirical wavelet transform (EWT), and empirical Fourier decomposition (EFD) were used to analyze the ULL signal. ULL series were decomposed into subseries, and the optimized outcome was used. All methods concluded that the ULL series has a steep downward trend. Signal reconstruction was performed, and it was inferred that EFD could not estimate the ULL series appropriately and had root-mean-square error (RMSE) = 12.26. Different from EFD, other methods performed better signal construction according to RMSE and error analysis. The mode-mixing issue was the last step in verifying the capabilities of signal-analyzing methods. Based on the power spectral density (PSD), it was seen that EMDs had mode-mixing problems and limitations in signal decomposition, whereas VMD and EWT did not have these issues. Results demonstrated that the present study has some limitations. Overall, it was concluded that VMD performed better in terms of RMSE, error analysis, reconstruction, mode-mixing problems, and PSD analysis while decomposing and extracting features from the ULL signal. HIGHLIGHTS Urmia Lake level (ULL) time series presented a steep downward trend, making it hard for signal processing methods to analyze accurately.; Variational mode decomposition (VMD) performed better than other methods in analyzing the ULL signal.; Empirical Fourier decomposition did not perform appropriately and was the weakest among other methods.; VMD had no mode-mixing issues contrary to empirical mode decompositions.;

Due to the lack of field monitoring points, the monitoring data is incomplete, making it difficult to obtain more complete characteristics of mining subsidence. To address this issue, a novel method for interpolating missing subsidence points is proposed. Firstly, the subsidence data are decomposed into multi-frequency domains utilizing the empirical mode decomposition (EMD) method. Subsequently, the missing subsidence data are reconstructed by combining the radial basis function (RBF) with phase space reconstruction technology. The interpolation accuracy is then analyzed under various conditions, including different mining degrees, sample numbers, and observation point locations. The results indicate that the interpolation accuracy increases with the mining degree coefficient. During the active stage of surface subsidence, a larger sample number correlates positively with higher interpolation accuracy. However, in the recession stage, the impact of sample number on interpolation accuracy is less pronounced compared to the timeliness of sample acquisition. The location of observation points has minimal effect on interpolation accuracy. By applying this method to the 833 working face in the Shanjiacun coal mine, the mean absolute percentage error (MAPE) is less than 20%, and the mean squared error (MSE) is below 0.5 mm, objectively demonstrating the method's effectiveness and engineering applicability. These research findings offer valuable technical support for mine safety production and ecological environment protection. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a comprehensive methodology for developing a vibration measurement system, with a primary focus on monitoring rolling element bearings, widely recognised as pivotal components in generic machine vibration monitoring. Through the utilisation of sensors to capture vibration data signals, the system aims to meticulously analyse these signals to discern any indications of bearing health deterioration, thus facilitating the early identification of potential pre-breakdown conditions. The proposed system initiates the process by internally decomposing the three-axis vibration signal, enabling subsequent transmission of processed data to a remote location for the identification of specific faults in the roller element bearings. Installation of a three-axis accelerometer on the bearing housing, positioned radially, facilitates the capture of raw vibration data along one axial and two radial directions. Moreover, efforts to enhance computational efficiency involve cascading multiple identical modules in a serial pipeline structure, allowing for iterative loop computation of intrinsic mode functions within Empirical Mode Decomposition (EMD). Real-time computation is achieved through this serial pipeline structure, necessitating two distinct clock frequencies: one at 12.5 MHz for sampling and storing 3-axis vibration data in ping pong buffers, and another at 40 MHz for reading and processing the data signal retrieved from these buffers. Validation of the proposed method is demonstrated through simulation results obtained via synthesised Verilog code on Xilinx Artix-7 FPGA (XC7A35T-1CPG236C), affirming its efficacy in real-time applications. [ABSTRACT FROM AUTHOR]

• Automatic selection of embedding dimension in Singular spectrum analysis is achieved. • Singular spectrum analysis yields the most stable and accurate sea level heights among the methods. • Singular spectrum analysis provides a better concentration of spectral power in the periodogram. In order to obtain reliable GNSS-R derived sea level heights (SLHs), it is crucial to extract accurate reflected signals from signal-to-noise ratio (SNR) data. In this study, Quadratic Fitting, Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Singular Spectrum Analysis (SSA) are adopted for extracting the reflected signals from SNR data, which are then analyzed with Lomb Scargle Periodogram (LSP) assisted with tidal harmonic analysis for GNSS-R SLHs. Three continuous GNSS stations including Onsala in Sweden, Friday Harbor in the United States, and Brest in France with tidal ranges of approximately 1 m, 3 m, and 7 m, respectively, were used in the study. The derived SLHs were evaluated against the nearby or co-located tide gauge records. The results reveal that the three proposed algorithms effectively cope with the multi-peaks spectrum problems when using the conventional Quadratic Fitting without a priori reflected height constraint, and SSA can provide the solutions with fewer outliers among the proposed methods with the RMSEs of 6.7 cm, 13.4 cm and 39.6 cm at Onsala, Friday Harbor, and Brest stations, respectively. Without implementing tidal harmonic analysis, SSA is the method capable of acquiring reliable SLHs among the stations, as evidenced by comparing with co-located gauge records, particularly at the Brest station. In summary, SSA not only distinguishes signals across different frequencies, but also orders the signal components according to their eigenvalues, demonstrating the potential to extract the reflected signals from SNR and enhance the stability and accuracy of GNSS-R applications. [ABSTRACT FROM AUTHOR]

Coxsackievirus B3 (CVB3) encodes proteinases that are essential for processing of the translated viral polyprotein. Viral proteinases also target host proteins to manipulate cellular processes and evade innate antiviral responses to promote replication and infection. While some host protein substrates of the CVB3 3C and 2A cysteine proteinases have been identified, the full repertoire of targets is not known. Here, we utilize an unbiased quantitative proteomics-based approach termed terminal amine isotopic labeling of substrates (TAILS) to conduct a global analysis of CVB3 protease-generated N-terminal peptides in both human HeLa and mouse cardiomyocyte (HL-1) cell lines infected with CVB3. We identified >800 proteins that are cleaved in CVB3-infected HeLa and HL-1 cells including the viral polyprotein, known substrates of viral 3C proteinase such as PABP, DDX58, and HNRNPs M, K, and D and novel cellular proteins. Network and GO-term analysis showed an enrichment in biological processes including immune response and activation, RNA processing, and lipid metabolism. We validated a subset of candidate substrates that are cleaved under CVB3 infection and some are direct targets of 3C proteinase in vitro. Moreover, depletion of a subset of TAILS-identified target proteins decreased viral yield. Characterization of two target proteins showed that expression of 3Cpro-targeted cleaved fragments of emerin and aminoacyl-tRNA synthetase complex-interacting multifunctional protein 2 modulated autophagy and the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway, respectively. The comprehensive identification of host proteins targeted during virus infection provides insights into the cellular pathways manipulated to facilitate infection. IMPORTANCE RNA viruses encode proteases that are responsible for processing viral proteins into their mature form. Viral proteases also target and cleave host cellular proteins; however, the full catalog of these target proteins is incomplete. We use a technique called terminal amine isotopic labeling of substrates (TAILS), an N-terminomics to identify host proteins that are cleaved under virus infection. We identify hundreds of cellular proteins that are cleaved under infection, some of which are targeted directly by viral protease. Revealing these target proteins provides insights into the host cellular pathways and antiviral signaling factors that are modulated to promote virus infection and potentially leading to virus-induced pathogenesis. [ABSTRACT FROM AUTHOR]

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The working environment of the mine is complex, and there are many interference noises. Microseismic signals and noises are mixed together, which interferes with the analysis of microseismic signals and affects the subsequent work of phase arrival picking and source location. Therefore, it is necessary to study the noise reduction of microseismic signals. Method: In view of the randomness and non-stationarity of microseismic signals, a signal denoising method based on the multi-layer joint is proposed in this paper. SVD (singular value decomposition) is used as the first layer denoising method to remove the white noise in the signal. EMD( Empirical Mode Decomposition) is used as the second layer noise reduction method to deal with random and non-stationary microseismic signals. As the third layer noise reduction method, the wavelet threshold eliminates the modal aliasing phenomenon generated by EMD. Result: The experimental analysis shows that the proposed method has a good noise reduction effect on the simulation signal and the actual microseismic signal with different signal-to-noise ratios. Conclusion: It overcomes the disadvantage of the poor noise reduction effect of a single noise reduction method, can greatly improve the signal-to-noise ratio of the signal, and has a high similarity with the original signal, which provides a new idea for microseismic signal noise reduction. [ABSTRACT FROM AUTHOR]

AbstractEmotion recognition using EEG is a difficult study because the signals’ unstable behavior, which is brought on by the brain’s complex neuronal activity, makes it difficult to extract the underlying patterns inside it. Therefore, to analyse the signal more efficiently, in this article, a hybrid model based on IEMD-KW-Ens (Improved Empirical Mode Decomposition-Kruskal Wallis-Ensemble classifiers) technique is used. Here IEMD based technique is proposed to interpret EEG signals by adding an improved sifting stopping criterion with median filter to get the optimal decomposed EEG signals for further processing. A mixture of time, frequency and non-linear distinct features are extracted for constructing the feature vector. Afterward, we conducted feature selection using KW test to remove the insignificant ones from the feature set. Later the classification of emotions in three-dimensional model is performed in two categories i.e. machine learning based RUSBoosted trees and deep learning based convolutional neural network (CNN) for DEAP and DREAMER datasets and the outcomes are evaluated for valence, arousal, and dominance classes. The findings demonstrate that the hybrid model can successfully classify emotions in multichannel EEG signals. The decomposition approach is also instructive for improving the model’s utility in emotional computing. [ABSTRACT FROM AUTHOR]

Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well as the development of computational biology, depends on the precise identification of these regions. In this paper, a hot spot prediction method called EC-PDH is proposed. First, we extracted features of these hot spots' solid solvent-accessible surface area (ASA) and secondary structure, and then the mean, variance, energy and autocorrelation function values of the first three intrinsic modal components (IMFs) of these conventional features were extracted as new features via the empirical modal decomposition algorithm (EMD). A total of 218 dimensional features were obtained. For feature selection, we used the maximum correlation minimum redundancy sequence forward selection method (mRMR-SFS) to obtain an optimal 11-dimensional-feature subset. To address the issue of data imbalance, we used the SMOTE-Tomek algorithm to balance positive and negative samples and finally used cat gradient boosting (CatBoost) to construct our hot spot prediction model for protein-DNA binding interfaces. Our method performs well on the test set, with AUC, MCC and F1 score values of 0.847, 0.543 and 0.772, respectively. After a comparative evaluation, EC-PDH outperforms the existing state-of-the-art methods in identifying hot spots. [ABSTRACT FROM AUTHOR]

This paper extracts and separates seasonal term of GPS (Global Positioning System) time series based on empirical mode decomposition and wavelet transformation. Through time series analysis of 9 GPS continuous stations in Dali, Yunnan, it is found that the vertical (U), north–south (N) and east–west (E) components of the relative motion have distinct annual and semi-annual period components. In the vertical direction, the U component has the strongest seasonal deformation characteristics, on the annual period term, between each station the correlation coefficient reaches 0.98, this is consistent with the relevant research results of many researchers; In horizontal direction, seasonal deformation is also more significant, the N component annual and semi-annual period signals are more obvious and the correlation coefficient is high, but the E component 9 stations signal are relatively scattered and poorly correlated. On the semi-annual period, the N and U directions have a very obvious and consistent semi-annual periodicity, and their two correlation coefficient numbers are 0.95 and 0.94 on average, respectively, the N and E are negatively correlated with a correlation coefficient of -0.98. In time series trend term, 9 stations show southeast movement in horizontal direction, but have great differences in vertical movement trend. Among them, YNLJ, YNYS, YNSD and YNLC are linear uplift movement with good consistency. YNYA, YNCX and YNJD show overall uplift movement, but with subsidence fluctuation, the trend of the three stations is very similar, with an average correlation coefficient reaches 0.8; XIAG station shows uplift movement as a whole, the relative motion trend in the E direction is very different from other stations in phase and amplitude, probably because it is closer to the Erhai lake and more susceptible to the influence of water level changes; YNYL also has different motion changes at different times, but the overall performance is subsidence motion. The analysis suggests that the GPS time series contains rich information on the seasonal deformation of the Earth's crust, and precipitation has an important role in influencing the seasonal deformation of continuous stations. [ABSTRACT FROM AUTHOR]

Against the backdrop of increasing downward pressure on the global economy, domestic economic development also faces many uncertainties, especially the production of the industrial sector, which responds more quickly and sensitively to external shocks and internal adjustments, making it more difficult to predict industrial added value. Electricity, as an important terminal consumption energy, is a barometer of economic prosperity. Using a decomposition integration approach, a combination prediction method based on LSTM models with electricity consumption data is constructed, and short-term predictions of provincial industrial added value are made. The results show that the EMD-LSTM model with external data can achieve better short-term prediction results. [ABSTRACT FROM AUTHOR]

In few-shot image classification (FSIC), the feature extraction module of the traditional convolutional neural networks is often constrained by the local nature of the convolutional kernel. As a result, it becomes challenging to handle global information and long-distance dependencies effectively. In order to address this problem, an innovative FSIC method is proposed in this paper, which is the integration of Swin Transformer and CSAM and Earth Mover's Distance (EMD) technology (STCE). We utilize the Swin Transformer network for image feature extraction, and perform CSAM attention mechanism feature weighting on the output feature map, while we adopt the EMD algorithm to generate the optimal matching flow between the structural units, minimizing the matching cost. This approach allows for a more precise representation of the classification distance between images. We have conducted numerous experiments to validate the effectiveness of our algorithm. On three commonly used few-shot datasets, namely mini-ImageNet, tiered-ImageNet, and FC100, the accuracy of one-shot and five-shot has reached the state of the art (SOTA) in the FSIC; the mini-ImageNet achieves an accuracy of 98.65 ± 0.1% for one-shot and 99.6 ± 0.2% for five-shot tasks, while tiered ImageNet has an accuracy of 91.6 ± 0.1% for one-shot tasks and 96.55 ± 0.27% for five-shot tasks. For FC100, the accuracy is 64.1 ± 0.3% for one-shot tasks and 79.8 ± 0.69% for five-shot tasks. On two commonly used few-shot datasets, namely CUB, CIFAR-FS, CUB achieves an accuracy of 83.1 ± 0.4% for one-shot and 92.88 ± 0.4% for five-shot tasks, while CIFAR-FS achieves an accuracy of 86.95 ± 0.2% for one-shot and 94 ± 0.4% for five-shot tasks. [ABSTRACT FROM AUTHOR]

This paper presents a study to accurately evaluate defects in concrete decks using ultrasonic pulse-echo signals. A reinforced concrete deck with void defects was designed and evaluated for validation, and a commercial ultrasonic pulse-echo (UPE) device was used to obtain the 2D images of the void defect inside the deck. The UPE image is based on the ultrasonic shear-wave test method and an extended synthetic aperture focusing technique (SAFT). To enhance the accuracy of the defect location in the SAFT imaging, the recorded A-scan data from UPE was analyzed using an advanced denoising approach and defect echo peak extraction, which are based on empirical modal decomposition, Hurst exponent characterization, and Hilbert envelope estimation. The results demonstrated that the location and depth of the void defect in the deck can be accurately assessed by using the developed approach. The new method provides quantitative information of the anomalies inside the deck, which can be used to calibrate the qualitative images of UPC devices with the SAFT. [ABSTRACT FROM AUTHOR]

Background: The search for effective antimicrobial agents to mitigate peri-implant infections remains a crucial aspect of implant dentistry. This study aimed to evaluate and compare the antimicrobial efficacy of i-PRF, A-PRF+, and enamel matrix derivative (EMD) on decontaminated rough and smooth titanium (Ti) discs. Materials and Methods: Rough and smooth Ti discs were coated with multispecies biofilm and thoroughly debrided using a chitosan-bristled brush. Subsequently, i-PRF, A-PRF+, and EMD were applied. Untreated discs served as control. Residual adherent bacteria present on the treated Ti discs were visualized by SEM and quantified using culture technique, and colony-forming units (CFUs) were measured after 48 h and 7 days. Results: i-PRF demonstrated better antimicrobial effectiveness on both smooth and rough implant surfaces as compared to A-PRF+ and EMD (p < 0.001). In all the experimental groups, smooth Ti discs displayed a greater reduction in microbes compared to rough Ti discs when treated with the biologics. The major reduction in CFU values was determined after seven days. Conclusions: i-PRF as a regenerative material may also be suitable for decontaminating implant surfaces, which could influence tissue healing and regenerative outcomes positively. [ABSTRACT FROM AUTHOR]

Purpose: In the winding process of chemical fiber production, the winding machine was responsible for winding the post-treated primary fiber. The winding machine clamp head was used as the direct winding part, and its running state would directly affect the quality and production efficiency of the chemical fiber cake. With the increasing complexity and precision of chemical fiber equipment, the traditional method of fault diagnosis relying on manual experience has been unable to meet the needs of intelligent production. The winding equipment also has some problems, such as few fault samples, strong background noise interference and large speed fluctuation. Therefore, the study of a better intelligent fault diagnosis method is of great significance to improve the winding reliability of the clamp head and ensure the quality and production efficiency of the chemical fiber cake. Methods: In this paper, a fault diagnosis method based on Empirical Mode Decomposition Order Spectrum of vibration signal and Dual-Channel Convolutional Neural Network (EMDOS-DCCNN) was proposed and the corresponding fault diagnosis model was designed. The trigger interval sampling scheme was designed according to the characteristics of the changing working conditions of the winder. The vibration data at the base of the winder's clamp head was collected by data acquisition equipment from Zhongli Winder in a chemical fiber production plant in Zhejiang Province as the real measured data for rolling bearing diagnosis. The strong background noise and the influence of the variable speed of the winder were removed by Empirical Mode Decomposition (EMD) reconstruction of the original signal and order spectrum analysis. The dual-channel convolutional neural network model was used for fault diagnosis, and the dual-channel data was fused to further improve the fault diagnosis accuracy. Order analysis and validation set confusion matrix were used to evaluate the diagnostic model proposed in this paper. Results and Conclusion: The model was trained with vibration data collected at three rotational speeds and verified on data sets collected at other rotational speeds. The results showed that the fault diagnosis model of rolling bearings based on EMDOS-DCCNN could effectively remove noise, and a single diagnosis model could be used to diagnose rolling bearings at different rotational speeds. In the model evaluation experiment, the accuracy of the dual-channel CNN model was stable at 98.6%, which was 3.1% higher than that of the single-channel model. It had high fault identification accuracy and adaptability at different speeds, and the model was suitable for variable working conditions and had better robustness. [ABSTRACT FROM AUTHOR]

Microalgae biofuels are considered a significant source of future renewable energy due to their efficient photosynthesis and rapid growth rates. However, practical applications face numerous challenges such as variations in environmental conditions, high cultivation costs, and energy losses during production. In this study, we propose an ensemble model called ELG, integrating Empirical Mode Decomposition (EMD), Long Short-Term Memory (LSTM), and Gradient Boosting Machine (GBM), to enhance prediction accuracy. The model is tested on two primary datasets: the EIA (U.S. Energy Information Administration) dataset and the NREL (National Renewable Energy Laboratory) dataset, both of which provide extensive data on biofuel production and environmental conditions. Experimental results demonstrate the superior performance of the ELG model, achieving an RMSE of 0.089 and MAPE of 2.02% on the EIA dataset, and an RMSE of 0.1 and MAPE of 2.21% on the NREL dataset. These metrics indicate that the ELG model outperforms existing models in predicting the efficiency of microalgae biofuel production. The integration of EMD for preprocessing, LSTM for capturing temporal dependencies, and GBM for optimizing prediction outputs significantly improves the model’s predictive accuracy and robustness. This research, through high-precision prediction of microalgae biofuel production efficiency, optimizes resource allocation and enhances economic feasibility. It advances technological capabilities and scientific understanding in the field of microalgae biofuels and provides a robust framework for other renewable energy applications.

To address the challenges of secure and reliable communication and system quality of service (QoS) prediction in intelligent production lines (IPL) in the Industrial Intelligent Internet of Things (II-IOT) environment, a redundant collaborative security model-based communication architecture is designed. First, the redundant collaborative security communication model is introduced to construct the network communication architecture of IPL, including the industrial-site mechanical floor, data awareness layer, and gateway and application layer. Then, to leverage the advantages of the empirical-mode decomposition (EMD) method and the bidirectional long short-term memory (BiLSTM) model in time-series data analysis and processing, an EMD-BiLSTM-based QoS prediction model is proposed that can synchronously achieve one-step and multi-step prediction of QoS attributes. The proposed model exhibits a prediction accuracy of up to 94.01% on the SourceForge dataset, with prediction, recall, and F1 values as high as 91.37, 90.60, and 90.99%, respectively. The proposed EMD-BiLSTM model can achieve better performance than the state-of-the-art QoS prediction models, indicating that the proposed model can be more effectively used to improve the reliable communication level of II-IoT.

The adjusted precision of economic values is essential in the global economy. In recent years, researchers have increased their interest in making accurate predictions in this type of time series; one of the reasons is that the characteristics of this type of time series makes predicting a complicated task due to its non-linear nature. The evolution of artificial neural network models enables us to research the suitability of models generated for other purposes, applying their potential to time series prediction with promising results. Specifically, in this field, the application of transformer models is assuming an innovative approach with great results. To improve the performance of this type of networks, in this work, the empirical model decomposition (EMD) methodology was used as data preprocessing for prediction with a transformer type network. The results confirmed a better performance of this approach compared to networks widely used in this field, the bidirectional long short term memory (BiLSTM), and long short term memory (LSTM) networks using and without EMD preprocessing, as well as the comparison of a Transformer network without applying EMD to the data, with a lower error in all the error metrics used: The root mean square error (RMSE), the root mean square error (MSE), the mean absolute percentage error (MAPE), and the R-square (R2). Finding a model that provides results that improve the literature allows for a greater adjustment in the predictions with minimal preprocessing.

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach–Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care. [ABSTRACT FROM AUTHOR]

The adjusted precision of economic values is essential in the global economy. In recent years, researchers have increased their interest in making accurate predictions in this type of time series; one of the reasons is that the characteristics of this type of time series makes predicting a complicated task due to its non-linear nature. The evolution of artificial neural network models enables us to research the suitability of models generated for other purposes, applying their potential to time series prediction with promising results. Specifically, in this field, the application of transformer models is assuming an innovative approach with great results. To improve the performance of this type of networks, in this work, the empirical model decomposition (EMD) methodology was used as data preprocessing for prediction with a transformer type network. The results confirmed a better performance of this approach compared to networks widely used in this field, the bidirectional long short term memory (BiLSTM), and long short term memory (LSTM) networks using and without EMD preprocessing, as well as the comparison of a Transformer network without applying EMD to the data, with a lower error in all the error metrics used: The root mean square error (RMSE), the root mean square error (MSE), the mean absolute percentage error (MAPE), and the R-square (R²). Finding a model that provides results that improve the literature allows for a greater adjustment in the predictions with minimal preprocessing. [ABSTRACT FROM AUTHOR]

In this study, a thorough analysis of the proposed approach in the context of emotion classification using both single-modal (A-13sbj) and multi-modal (B-12sbj) sets from the YAAD dataset was conducted. This dataset encompassed 25 subjects exposed to audiovisual stimuli designed to induce seven distinct emotional states. Electrocardiogram (ECG) and galvanic skin response (GSR) biosignals were collected and classified using two deep learning models, BEC-1D and ELINA, along with two different preprocessing techniques, a classical fourier-based filtering and an Empirical Mode Decomposition (EMD) approach. For the single-modal set, this proposal achieved an accuracy of 84.43±30.03, precision of 85.16±28.91, and F1-score of 84.06±29.97. Moreover, in the extended configuration the model maintained strong performance, yielding scores of 80.95±22.55, 82.44±24.34, and 79.91±24.55, respectively. Notably, for the multi-modal set (B-12sbj), the best results were obtained with EMD preprocessing and the ELINA model. This proposal achieved an improved accuracy, precision, and F1-score scores of 98.02±3.78, 98.31±3.31, and 97.98±3.83, respectively, demonstrating the effectiveness of this approach in discerning emotional states from biosignals. [ABSTRACT FROM AUTHOR]

An optimization algorithm for federated learning, equipped with an incentive mechanism, is introduced to tackle the challenges of excessive iterations, prolonged training durations, and suboptimal efficiency encountered during model training within the federated learning framework. Initially, the algorithm establishes reputation values that are tied to both time and model loss metrics. This foundation enables the creation of incentive mechanisms aimed at rewarding honest nodes while penalizing malicious ones. Subsequently, a bidirectional selection mechanism anchored in blockchain technology is developed, allowing smart contracts to enroll nodes with high reputations in training sessions, thus filtering out malicious clients and enhancing local training efficiency. Furthermore, the integration of the Earth Mover's Distance (EMD) mechanism serves to lessen the impact of non-IID (non-Independent and Identically Distributed) data on the global model, leading to a reduction in the frequency of model training cycles and an improvement in model accuracy. Experimental results confirm that this approach maintains high model accuracy in non-IID data settings, outperforming traditional federated learning algorithms. [ABSTRACT FROM AUTHOR]

An optimization algorithm for federated learning, equipped with an incentive mechanism, is introduced to tackle the challenges of excessive iterations, prolonged training durations, and suboptimal efficiency encountered during model training within the federated learning framework. Initially, the algorithm establishes reputation values that are tied to both time and model loss metrics. This foundation enables the creation of incentive mechanisms aimed at rewarding honest nodes while penalizing malicious ones. Subsequently, a bidirectional selection mechanism anchored in blockchain technology is developed, allowing smart contracts to enroll nodes with high reputations in training sessions, thus filtering out malicious clients and enhancing local training efficiency. Furthermore, the integration of the Earth Mover's Distance (EMD) mechanism serves to lessen the impact of non-IID (non-Independent and Identically Distributed) data on the global model, leading to a reduction in the frequency of model training cycles and an improvement in model accuracy. Experimental results confirm that this approach maintains high model accuracy in non-IID data settings, outperforming traditional federated learning algorithms.

Purpose: The aim of the present systematic review with meta-analysis was to investigate the clinical effectiveness of EMD (enamel matrix derivative) using a minimally invasive surgical technique (MIST) or flapless approach for the treatment of severe periodontal probing depths. Materials and Methods: A systematic review of the literature including searches in PubMed/Medline, Cochrane Library, Google Scholar, and Grey Literature databases as well as manual searches was performed on September 1st, 2021. Studies utilising EMD in a non-surgical or minimally invasive approach were included. The eligibility criteria comprised randomised controlled trials (RCTs) comparing minimally-invasive/flapless approaches with/without EMD for the treatment of probing depths >5 mm. Results: From 1525 initial articles, 7 RCTs were included and 12 case series discussed. Three studies investigated a MIST approach, whereas 3 studies utilised a flapless approach. One study compared EMD with either a MIST or a flapless approach. The RCTs included ranged from 19-49 patients with at least 6 months of follow-up. While 5 of the studies included smokers, patients smoking >20 cigarettes/day were excluded from the study. The meta-analysis revealed that EMD with MIST improved recession coverage (REC) and bone fill (BF) when compared to MIST without EMD. However, no difference in CAL or PD was observed between MIST + EMD vs MIST without EMD. No statistically significant advantage was found for employing the EMD via the flapless approach. Conclusions: Implementing EMD in MIST procedures displayed statistically significant improvement in REC and BF when compared to MIST alone. These findings suggest that MIST in combination with EMD led to improved clinical outcomes while EMD employed in nonsurgical flapless therapy yielded no clinical benefits when compared to nonsurgical therapy alone without EMD. More research is needed to substantiate these findings. [ABSTRACT FROM AUTHOR]

The time series of exchange rate fluctuations are characterized by non-stationary and nonlinear features, and forecasting using traditional linear or single-machine models can cause significant bias. Based on this, the authors propose the combination of the advantages of the EMD and LSTM models to reduce the complexity by analyzing and decomposing the time series and forming a new model, EMD-LSTM-SVR, with a stronger generalization ability. More than 30,000 units of data on the USD/CNY exchange rate opening price from 2 January 2015 to 30 April 2022 were selected for an empirical demonstration of the model's accuracy. The empirical results showed that the prediction of the exchange rate fluctuation with the EMD-LSTM-SVR model not only had higher accuracy, but also ensured that most of the predicted positions deviated less from the actual positions. The new model had a stronger generalization ability, a concise structure, and a high degree of ability to fit nonlinear features, and it prevented gradient vanishing and overfitting to achieve a higher degree of prediction accuracy. [ABSTRACT FROM AUTHOR]

The study on forecasting demand for online car-hailing holds substantial implications for both online car-hailing platforms and government agencies responsible for traffic management. This research proposes an enhanced Empirical Mode Decomposition Long-short Term Memory Neural Network (EMD-LSTM) model. EMD technique reduces noise and extracts stable intrinsic mode functions (IMF) from the original time series. Genetic algorithm is deployed to improve the K-Means clustering for determining optimal clusters. These sub time series serve as input for the prediction model, with combined results giving final predictions. Experimental data from Didi includes Haikou’s car-hailing orders from May to October 2017 and Beijing’s from January to May 2020. Results show improved EMD-LSTM reduces instability and captures characteristics better. Compared to unmodified EMD-LSTM, RMSE decreases by 3.50%, 6.81%, and 6.81% for the three datasets, and by 30.97%, 20%, and 9.24% respectively compared to single LSTM model. [ABSTRACT FROM AUTHOR]