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This work modifies the architecture of conventional CNN with the integration of Multi-resolution Analysis (MRA) in a CNN framework for Diabetic Retinopathy (DR) diagnosis and grading. Here, the HF sub-bands are subjected to optimized activations and are directly fed to the fully connected layers, as it encompasses edge features. Unlike FD-Relu, the proposed function preserves significant negative coefficients, compared to the S-Relu, the proposed third-order S-Relu is optimized such that it sustains the activations in the range suitable for the wavelet coefficients. The coefficients of higher-order terms of the proposed 3rd-order S-Relu are optimized with PSO, fitting the maximum energy of the wavelet sub-bands to ensure High Frequency (HF) edge preservation. The authors re-architecture 3 different CNNs published in the Retinal Image analysis field, with spatial and wavelet inputs with optimized activations. The highest accuracy of 96% is attained with the AlexNet re-architecture, with 35,126 fundus images secured from the Kaggle dataset. As we can infer the proposed re-architecture wavelet CNN outperformed the multiscale shallow CNNs, multiscale attention net, and stacked CNNs with a 6.6, 0.3, 0.7 per cent increase in accuracy. The entire implementation of the wavelet CNN is made available under source code.

Abstract The determination of bedrock depth is crucial across various earth sciences and related fields. Geophysical techniques, notably the continuous wavelet transform, are increasingly employed to map subsurface bedrock structures. This method allows the transformation of 1D time domain seismic reflection signals into a 2D image, providing time–frequency representations for feature extraction. Leveraging the advantages of wavelet theory, particularly the Haar wavelet known for its simplicity and compact support, this study utilizes continuous Haar wavelet local maxima lines to enhance bedrock analysis. Seismic reflection data is acquired using an extremely sensitive geophone as a receiver along with a high dynamic range data logger. The Common Midpoint (CMP) approach, coupled with optimized offset distances, is utilized to ensure robust data quality and signal strength. Simple sources, such as a sledgehammer, generate sound waves for data collection prior to borehole drilling, correlating acquired data with lithogeological information. This research emphasizes the significance of adjusting parameters like sampling frequency, choice of wavelet, and optimal offset distances. The methodology employed, involving a single geophone, a one-channel data recorder, and a basic sledgehammer as a sound source, offers an inexpensive, straightforward, and non-invasive approach. The study demonstrates the effectiveness of Haar wavelet local maxima lines in accurately determining bedrock depth, producing results closely aligned with both predicted and actual depths.

In this paper, a hybrid method for solving time-fractional Black-Scholes equation is introduced for option pricing. The presented method is based on time and space discretization. A second order finite difference formula is used to time discretization and space discretization is done by a spectral method based on Chelyshkov wavelets and an operational process by defining Chelyshkov wavelets operational matrices. Convergence and error analysis for Chelyshkov wavelets approximation and also for the proposed method are discussed. The method is validated and its accuracy, convergency and efficiency are demonstrated through some cases with given accurate solutions. The method is also utilize for pricing various European options conducted by a time-fractional Black-Scholes model

Abstract This study focused on detecting the reflections of healing and change in cortex activation in full-face transplantation and lesions patients on EEG activity. Face transplant patients have facial lesions before transplantation and, to identify pre-face transplant patients' brain activity in the absence of pre-transplant recordings, we used data obtained from pre-transplant facial lesion patients. Ten healthy, four facial lesion and three full-face transplant patients participated in this study. EEG data recorded for four different sensory stimuli (brush from the right face, right hand, left face, and left-hand regions) were analyzed using wavelet packet transform method. EEG waves were analyzed for standard bands. Our findings indicate significant change in the 2–4 Hz frequency range which may be a result of ongoing or previous cortical reorganization for face lesion and transplant patients. Alterations of the delta wave seen in patients with facial lesion and face transplant can also be explained by the intense central plasticity. Our findings show that the delta band differences might be used as a marker in the evaluation of post-transplant cortical plasticity in the future.

Background. Migraine disease is the second most common cause of headaches. Despite the high prevalence, the exact etiology of migraine is yet unknown. In this study, to evaluate the behavior change of electroencephalography (EEG) signals in migraine patients, various features of the EEG signals of migraine patients and healthy controls (HCs) were extracted and compared. Methods. This cross-sectional analytical study was conducted on 21 HCs and 18 migraine patients. Various features, such as fractal dimension (FD), approximate entropy (ApEn), and largest Lyapunov exponent (LLE), were calculated from the EEG signals of migraine patients and HCs. Then different frequency sub-bands of delta, theta, alpha, beta, and gamma were extracted using the wavelet transform, and the energy of these sub-bands was computed. By calculating the mean and variance of the features and applying statistical tests, the feature changes were compared between two groups, and channels with significant differences were identified. Results. The mean of ApEn, FD and energy of all frequency sub-bands in most of the analyzed channels was higher in migraine patients than in HCs. The mean LLE was mostly lower in migraine patients than in healthy controls. According to the statistical tests, the energy of theta and delta frequency sub-bands with 36 and 35 channels was the feature with the highest number of channels, with a significant difference. In this study, P values less than 0.05 were considered statistically significant. Conclusion. Migraine patients may have a less sophisticated brain dynamic system due to an increase in irregularity and randomness, as indicated by an increase in ApEn and a decrease in FD in their EEG signals compared to HCs. Anxiety, tension, and other intense sentiments and emotions, as well as the creation of new neural circuits in the brain, can all contribute to an overall increase in energy across all frequency sub-bands in migraine patients. Practical Implications. Considering the EEG signal behavior as the response of a dynamic system, we can say that the brain function of migraine patients, even in the inter-ictal phase, leaves the definite chaotic state, which is a healthy brain behavior, and enters the random state.

Corrosion on a plate of magnesium alloy WZ73 in contact with 0.001 mol/L NaCl solution was analysed by long-range testing using the acoustic emission (AE) technique. At a distance d = 1.66 m to the source, the Lamb wave propagation of resulting AE events was examined and used for corrosion source location. As a first step, pencil lead break test was carried out and corresponding Lamb wave analysis served as reference to the varying corrosion signals of lower intensity. Additionally, the plate was excited by a piezo transducer for imitating a selected corrosion event. For analysis of the propagating wave field, the plate surface was measured by a scanning laser Doppler vibrometer (SLDV), allowing a high-resolution corrosion signal reconstruction and path tracking of direct and reflected Lamb wave packets. Based on the SLDV results, a deeper understanding of the complex wave field propagation during the sensor-based corrosion analysis was achieved.

During geopolitical crises, the price stability of agricultural commodities is critical for national security. Understanding the dynamics of pricing power between the U.S. and China and how it varies over time can help smaller nations navigate unpredictable moments. This study uses a unified framework and wavelet approach to examine soybean price discovery in the U.S. and China from the standpoints of price interdependence and information flows. We begin by illustrating the integrated link between the soybean futures markets in the U.S. and China, which includes multiple structural breaks. The pricing difference between the two nations acts as the primary information spillover route for their integrated relationship. Furthermore, we show that the direction and degree of information spillover change dramatically in proportion to the strength of the U.S.–Chinese soybean interaction. Finally, we find that China’s recent retaliatory tax on the U.S. soybeans gave the Chinese market a more powerful position in soybean futures price discovery. After the first-stage trade deal was reached, and during the epidemic phase of the coronavirus pandemic, the pricing power of the U.S. soybean market showed no signs of full recovery.

Food security is crucial on a global scale as it provides essential health, social, and economic advantages to the human population. Therefore, to achieve the sustainable development goal of zero hunger (Goal 2), it is imperative to assess the progress of food security, particularly in developing countries. Thus, this reflects on the relative factors affecting food security in Nigeria from 1980 to 2022. The study utilized advanced econometrics approaches such as Quantile Autoregressive Distributed Lag (QARDL) and Wavelet Coherence (WTC) to analyze the data. Food security is measured using the food production index (FPI), while other variables such as globalization (GLOB), population growth (POP), gross domestic product (GDP) as a measure of economic growth, and inflation are considered. The results of the Quantile Autoregressive Distributed Lag analysis indicate that in the short-term, globalization has a beneficial impact on FPI at the 0.25 quantile, but it has a detrimental effect on food security at the 0.50 and 0.75 quantiles. Nevertheless, over a prolonged period, the effect is detrimental in all quantiles. Additionally, in the short-term, FPI experiences a beneficial effect from GDP in all quantiles. Over time, GDP has a positive effect on all levels of income distribution. However, in the short-term, the size of the POP has a negative effect on FPI for the 0.25 to 0.75 quantiles. Further discussion is dedicated to policy options and their implications.

IntroductionRemoving motion artifacts (MAs) from functional near-infrared spectroscopy (fNIRS) signals is crucial in practical applications, but a standard procedure is not available yet. Artificial neural networks have found applications in diverse domains, such as voice and image processing, while their utility in signal processing remains limited.MethodIn this work, we introduce an innovative neural network-based approach for online fNIRS signals processing, tailored to individual subjects and requiring minimal prior experimental data. Specifically, this approach employs one-dimensional convolutional neural networks with a penalty network (1DCNNwP), incorporating a moving window and an input data augmentation procedure. In the training process, the neural network is fed with simulated data derived from the balloon model for simulation validation and semi-simulated data for experimental validation, respectively.ResultsVisual validation underscores 1DCNNwP’s capacity to effectively suppress MAs. Quantitative analysis reveals a remarkable improvement in signal-to-noise ratio by over 11.08 dB, surpassing the existing methods, including the spline-interpolation, wavelet-based, temporal derivative distribution repair with a 1 s moving window, and spline Savitzky-Goaly methods. Contrast-to-noise ratio (CNR) analysis further demonstrated 1DCNNwP’s ability to restore or enhance CNRs for motionless signals. In the experiments of eight subjects, our method significantly outperformed the other approaches (except offline TDDR, t

This study investigates the VAR time series data of the overall expenditures and income in the Kurdistan ‎Region of Iraq. It applies multivariate wavelet shrinkage within the VAR model, comparing it to ‎traditional methods to identify the most appropriate model. The chosen model will then be used to ‎predict general expenditures and revenues for the years 2022-2026. The analysis involved assessing the ‎stationarity of the expenditure and revenue time series, which are interrelated variables during the ‎interval 1997-2021, and identifying the overall trend through differencing to achieve stationarity. The ‎proposed method incorporated multivariate wavelet shrinkage in the VAR model to address data ‎contamination in expenditures and revenue using various wavelets like Coiflets, Daubechies, Symlets, ‎and Fejér–Korovkin at different orders. Threshold levels were estimated using the SURE method and soft ‎thresholding rules to denoise the data for the following analysis within the VAR model. Model selection ‎was based on Akaike and Bayes information criteria. The analysis, conducted using MATLAB, indicated ‎the superiority of the proposed method over traditional methods, forecasting a continued rise in ‎expenditures and revenues for the Iraqi Kurdistan region from 2022 to 2026. The findings suggest that ‎advanced techniques can offer more accurate economic forecasts, benefiting regional planning and ‎policy-making.‎

The single-level 2D discrete wavelet transform method is a powerful technique for effectively removing Gaussian noise from natural images. Its effectiveness is attributed to its ability to capture a signal's energy at low energy conversion values, allowing for efficient noise reduction while preserving essential image details. The wavelet noise reduction method mitigates the noise present in the waveform coefficients produced by the discrete wavelet transform. In this study, three different wavelet families—Daubechies (db7), Coiflets (coif5), and Fejér-Korovkin (fk4)—were evaluated for their noise removal capabilities using the Bayes shrink method. This approach was applied to a set of images, and the performance was analyzed using the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. Our results demonstrated that among the wavelet families tested, the Fejér-Korovkin (fk4) wavelet consistently outperformed the others. The fk4 wavelet family yielded the lowest MSE values, indicating minimal reconstruction error, and the highest PSNR values, reflecting superior noise suppression and better image quality across all tested images. These findings suggest that the fk4 wavelet family, when combined with the Bayes shrink method, provides a robust framework for Gaussian noise reduction in natural images. The comparative analysis highlights the importance of selecting appropriate wavelet families to optimize noise reduction performance, paving the way for further research and potential improvements in image denoising techniques.

The type-Silurian Cellon section in the Carnic Alps in Austria underpins much of the current Silurian conodont zonations, forming the basis for the Silurian timescale. However, the Silurian record of the Cellon section lacks radiometric and astrochronological age constraints, making it difficult to gain insights into the processes pacing Silurian (anoxic) events. To attain age constraints and investigate the pacing Silurian (anoxic) events by astronomical cycles, a cyclostratigraphic study was conducted on high-resolution pXRF (CaO, Al2O3, and Fe2O3) and induration records spanning the Ludlow and Pridoli parts of the Cellon section. Astronomical cycles ranging from precession to the 405-kyr eccentricity cycle were first recognised visually in the field and in proxy records. The visual detection of astronomical cycles served as an input for the WaverideR R package, enabling the tracking of the 405-kyr eccentricity period in each proxy’s continous wavelet transform scalograms. These tracked period curves were combined with external age controls through multiple Monte Carlo simulations, generating an (absolute) age model. This age model is used to assign ages and durations and their respective uncertainties to a hiatus in the Ludfordian, conodont zones, lithological units, geochronological units and events, yielding new ages for Silurian stage boundaries (e.g., Gorstian-Ludfordian boundary at 425.92 ± 0.65 Ma, the Ludfordian-Pridoli boundary at 423.03 ± 0.53 Ma, the Silurian-Devonian boundary at 418.86 ± 1.02 Ma), and new durations for the Ludfordian at 2.89 ± 0.35 Myr and Pridoli at 4.24 ± 0.46 Myr. Furthermore, the imprint of astronomical cycles in the Cellon section itself indicates that the Linde, Klev and Silurian-Devonian boundary events all occur after a 2.4-Myr eccentricity node, indicating pacing by astronomical forcing, similar to other Devonian and Cretaceous anoxic events. The Lau event, however, does not appear to coincide with a 2.4-Myr eccentricity node.

Abstract The assessment of hydrocarbon flow through seismic and well-log data presents a persistent challenge in determining porosity. The acoustic impedance section provides a visual representation of the layers, while the raw seismic data showcase the subsurface reflectors that exist within the rock layers. The accuracy of acoustic impedance is widely acknowledged to surpass that of seismic data as a representation of reality. The primary objective of this study is to convert seismic reflector data into acoustic impedance values, which provide insights into the layer properties based on lithology. This approach enhances the accuracy of seismic inversion results by aligning them more closely with actual geological conditions. Seismic inversion is employed to ascertain the physical characteristics of the rock, including acoustic impedance and porosity. Carbonate reservoirs are recognised for their complex pore structures and heterogeneity, which present difficulties in their characterisation. The objective of this research is to predict the porosity and identify the reservoir within the dense carbonate reservoirs in Central Luconia, Sarawak. These objectives are achieved by employing a porosity and acoustic impedance cross-plot and improved precision and predictability through the integration of seismic attribute interpretation and deterministic seismic inversions. The uniqueness of our approach stems from the incorporation of various geophysical techniques to detect reservoirs that have hydrocarbon deposits. A correlation is observed between seismic inversion acoustic impedance and porosity within the zone of interest, indicating an estimated porosity range of 10–35%. The analysed area demonstrates the possibility of containing a hydrocarbon based on the observed relationship between porosity and impedance, as well as the outcomes of the inversion analysis.

Blind deblurring of optical remote sensing images has been a longstanding challenge. In recent years, many learning-based deblurring algorithms have been greatly developed. However, these methods often suffer from losing image texture details and some artificial artifacts under low signal-to-noise ratio (SNR) conditions. To tackle this challenge, we introduce an innovative end-to-end noise-robust blind deblurring algorithm based on the diffusion model joined with a denoising module and a wavelet-enhanced conditional embedding mechanism. Experiments have verified the effectiveness of our method. Compared to the image blind deblurring algorithms based on the diffusion model, the proposed algorithm demonstrates better performance in terms of quantitative metrics peak signal-to-noise ratio and structural similarity index. Compared to all the comparative algorithms, the proposed algorithm exhibits significant advantages in quantitative metrics learned perceptual image patch similarity, Fréchet inception distance, and natural image quality evaluator and shows superior visual effects in restoring texture details in the restored images, especially in challenging low SNR conditions.

Assessing changes in human attention states via noninvasive physiological signals poses a significant challenge. Research has often associated the migration of mitochondrial superoxide radicals with electrical skin signals and physiological state. This study proposes a novel method to identify meaningful features for discerning variations in attention states by examining skin potential (SP) signals from specific features. This research project began with gathering SP signals. Next, Wavelet Packet Transform (WPT) and other approaches are applied to conduct an energy analysis across various frequency bands, which allows for the extraction of time- and frequency-domain features from the SP signals, and the potential of these features to differentiate human attention states is then examined via regression-based classifiers. Feature selection refinement is accomplished through statistical tests and Linear Support Vector Machines (SVM) with Recursive Feature Elimination (RFE). The focus is on the discriminative power of a selected set of primary features to distinguish human attention states. The designed experiment revealed significant variations in SP signal features when the subjects experienced shifts in their attention states. These features encompass measures such as first- and second-order derivative sequences, wavelet energy, wavelet coefficient, and power spectral density in different frequency bands. The core significance of this research lies in its focus on the feature selection of the SP signal, which yields a set of highly impactful features contributing to the distinction of attention states. This study underscores the potential of classifier models to effectively distinguish attention states, particularly through the examination of critical features, such as the wavelet energy of SP signals within certain frequency bands. These features may be relevant to several psychological mechanisms that reinforce the relationship between physiological signals and cognitive state. The insights derived from this investigation deepen the comprehension of human attention states and set the groundwork for more granular future explorations of SP signals.

Abstract This study aimed to investigate the effects of caffeine ingestion on anaerobic performance and muscle activity in young athletes. In this randomized, double-blind, and placebo-controlled study, ten highly trained male post-puberal futsal players aged 15.9 ± 1.2 years conducted two laboratory sessions. Athletes performed the Wingate test 60 min after ingestion of caffeine (CAF, 6 mg/kg body mass) or placebo (PL, dextrose) (blinded administration). Peak power, mean power, and the fatigue index were assessed. During the performance of the Wingate test, electromyographic (EMG) data were recorded from selected lower limbs muscles to determine the root mean square (RMS), mean power frequency (MPF), and median power frequency (MDPF) as frequency domain parameters and wavelet (WT) as time-frequency domain parameters. Caffeine ingestion increased peak (0.80 ± 0.29 W/Kg; p = 0.01; d = 0.42) and mean power (0.39 ± 0.02 W/Kg; p = 0.01; d = 0.26) but did not significantly affect the fatigue index (52.51 ± 9.48%, PL: 49.27 ± 10.39%; p = 0.34). EMG data showed that the MPF and MDPF parameters decreased and the WT increased, but caffeine did not have a significant effect on these changes (p > 0.05). Moreover, caffeine ingestion did not significantly affect RMS changes in the selected muscles (p > 0.05). Here we showed that acute caffeine ingestion improved anaerobic performance without affecting EMG parameters in young male futsal athletes.

To support the roadmap for coexistence with Covid-19, contactless biometrics is needed as an individual identity verification technology in daily activities such as control systems, recording attendance at offices/schools/agencies and access rights to a room. An example of contactless biometrics is palm vein-based biometrics. Because it is contactless, this biometric system does not require direct contact between the user and the sensor device, providing several advantages in terms of comfort during acquisition and is more hygienic. In the palm vein recognition system, the palm vein pattern can be considered as a texture feature. Therefore, this study proposes a contactless biometric system based on palm vein recognition using the Local Line Binary Pattern method to extract texture features of palm vein images resulting from the decomposition of the 2D Wavelet Transformation, so as to produce a small texture descriptor that is compatible with the texture characteristics of thin veins. The proposed texture feature extraction method has been tested using the fuzzy k-NN classification method on 600 palm images with a CRR accuracy of 95.0% with a computation time of 0.057 seconds.

The weather conditions in one location can affect consumption patterns and the volume of electricity load demand (ELD) on power systems. This paper presents a wavelet-regression method to analyse the characteristic of ELD in Indonesia towards weather variables under tropical climate namely temperature, solar radiation exposure, humidity and wind speed using daily data from 2018 to 2020. The city of Makassar is used as a case study. The continuous wavelet transform (CWT) and wavelet transform coherence (WTC) analyses reveal that the variability of ELD, solar radiation and humidity showed high fluctuations within a period of 2 to 32 days. Strong relationships are found between the load and three weather variables (temperature, solar radiation exposure and humidity) which are represented in the colourful plots. Another result from multiple linear regression analysis for the whole period shows temperature has the highest impact on the volume of ELD, followed by solar radiation, and humidity. During the seasonal period, the impact of the seasons on the ELD is more pronounced in the rainy season compared to the dry season. The presented results are helpful for power management to meet ELD in Indonesia.

Detecting Traumatic Brain Injuries (TBI) through imaging remains challenging due to limited sensitivity in current methods. This study addresses the gap by proposing a novel approach integrating deep-learning algorithms and advanced image-fusion techniques to enhance detection accuracy. The method combines contextual and visual models to effectively assess injury status. Using a dataset of repeat mild TBI (mTBI) cases, we compared various image-fusion algorithms: PCA (89.5%), SWT (89.69%), DCT (89.08%), HIS (83.3%), and averaging (80.99%). Our proposed hybrid model achieved a significantly higher accuracy of 98.78%, demonstrating superior performance. Metrics including Dice coefficient (98%), sensitivity (97%), and specificity (98%) verified that the strategy is efficient in improving image quality and feature extraction. Additional validations with “entropy”, “average pixel intensity”, “standard deviation”, “correlation coefficient”, and “edge similarity measure” confirmed the robustness of the fused images. The hybrid CNN-ViT model, integrating curvelet transform features, was trained and validated on a comprehensive dataset of 24 types of brain injuries. The overall accuracy was 99.8%, with precision, recall, and F1-score of 99.8%. The “average PSNR” was 39.0 dB, “SSIM” was 0.99, and MI was 1.0. Cross-validation across five folds proved the model’s “dependability” and “generalizability”. In conclusion, this study introduces a promising method for TBI detection, leveraging advanced image-fusion and deep-learning techniques, significantly enhancing medical imaging and diagnostic capabilities for brain injuries.

Neonatal electrocardiogram (ECG) monitoring is an important diagnostic tool for identifying cardiac issues in infants at birth. Long-term remote neonatal dry-electrode ECG monitoring solutions can be an additional step for preventive healthcare measures. In these solutions, power and computationally efficient embedded signal processing techniques for denoising newborn ECGs can assist in increasing neonatal medical wearable time. Wavelet denoising is an appropriate denoising mechanism with low computational complexity that can be implemented on embedded microcontrollers for long-term remote ECG monitoring. Discrete wavelet transform (DWT) denoising for neonatal dry-electrode ECG using different wavelet families is investigated. The wavelet families and mother wavelets used include Daubechies (db1, db2, db3, db4, and db6), symlets (sym5), and coiflets (coif5). Different levels of added white Gaussian noise (AWGN) were added to 19 newborn ECG signals, and denoising was performed to select the appropriate wavelets for neonatal dry-electrode ECG. The selected wavelets then undergo real noise additions of baseline wander and electrode motion to determine their robustness and accuracy. Signal-to-noise ratio (SNR), mean squared error (MSE), and power spectral density (PSD) are used to examine denoising performance. db1, db2, and db3 wavelets are eliminated from analysis where the 30 dB AWGN led to negative SNR improvement for at least one newborn ECG, removing important ECG information. db4 and sym5 are eliminated from selection due to their different waveform morphology compared to the dry-electrode newborn ECG’s QRS complex. db6 and coif5 are selected due to their highest SNR improvement and lowest MSE of 6.26 × 10−6 and 1.65 × 10−7 compared to other wavelets, respectively. Their wavelet shapes are more like a newborn ECG’s QRS morphology, validating their selection. db6 and coif5 showed similar denoising performance, decreasing electrode motion and baseline wander noisy ECG signals by 10 dB and 14 dB, respectively. Further denoising of inherent dry-electrode noise is observed. DWT with coif5 or db6 wavelets is appropriate for denoising newborn dry-electrode ECGs for long-term neonatal dry-electrode ECG monitoring solutions under different noise types. Their similarity to newborn dry-electrode ECGs yields accurate and robust reconstructed denoised newborn dry-electrode ECG signals.

This study employs the wavelet coherence and the spillover index methodologies to compare time-varying relationship between green sukuk (GSI) and green bonds (SPGB) with S&P 500, FTSE 100, KBW NASDAQ Financial Technology Index and Bitcoin between October 2019, and December 2022. While the predominant tonality of the coherence of GSI and SPGB was weak, GSI and SPGB failed to offset spillover during the covid-19 pandemic. Overall, GSI exhibited superior diversification and hedging performance. The spillover index results unveiled the influence of bearish market conditions on the intensity of market connectedness, prompting temporal transmission of risks between markets which can last up to eight days following the emergence of a shock. GSI slightly outperformed SPGB in portfolio diversification amid low total market connectedness, less net spillover, and less net-pairwise spillover as revealed by the findings of the spillover index. A combination of GSI or SPGB with S&P 500, FTSE 100, KBW NASDAQ Financial Technology Index, and Bitcoin under the same investment portfolio is likely to offer long-term investors diversification and hedging opportunities.

Abstract The analysis of underwater discharge signals is of great significance for its application. Wavelet-based de-noising and analysis technology is an effective means to study underwater discharge signals. The selection of wavelets is the key to the accuracy of wavelet analysis. A scheme of wavelet selection is provided in this paper. Based on the signal characteristics and actual noise, the reference target signal and noisy signal are constructed to ensure the accuracy of wavelet performance evaluation. Cross-correlation coefficient, root mean square error, signal-to-noise ratio, and smoothness are chosen as evaluation indexes and fused by the coefficient of variation method. The selected optimal wavelet is used to process the underwater wire-guided discharge signals. The results show that the scheme is feasible and practical.

Hydraulic transient analysis allows the condition assessment of pipeline systems by the measurement of a system's transient pressure response subject to input pressure excitations. The detection of a pressure wave's arrival time and amplitude at one or more sections can be used to detect unexpected anomalies, such as leaks, blockages, or corroded sections. Wave separation approaches, based on signal processing techniques involving two sensors, enable a directional attribution to any measured pressure perturbations. Being able to determine the direction of origin of a perturbation through a signal-splitting approach greatly facilitates anomaly detection through the resolution of this ambiguity. The signal-splitting procedure can be sensitive to the analysis conditions (i.e. the signal processing procedure used, the presence of noise within the signal, and the spacing of the sensors) and, as a result, produce spurious results. This paper explores this issue and proposes, and analyses, a range of strategies to improve the signal-splitting results. The strategies explored involve the consideration of alternative time- and frequency-domain formulations; the use of filters and wavelet to condition the signal; and processing the time-shifted differenced signal as opposed to the original raw signal. Results are presented for a range of numerical and laboratory systems. HIGHLIGHTS The pressure signals acquired at two measurement sections can be separated, pointing out positive and negative traveling wave components.; The separation procedure can be helpful in the transient-based diagnosis in complex systems.; Different techniques are developed in time and frequency domains and tested on numerical and laboratory experiments.; Wavelet transform of separated signals is also derived.;

The anesthetic dosing procedure is a key element of safe surgical practice, where it is paramount to ensure sufficient dosing of the anesthetic agent to the patient in order to reach the desired depth of sedation for the necessary procedure. One means of monitoring the depth of anesthesia (DoA) involves the use of the bispectral index (BIS), which decodes electroencephalography (EEG) signals acquired from the frontal cortex in a continuous fashion. The shortcomings of this include the complexity of the decoding of EEG signals, insensitivity to certain anesthetic agents, and the costly nature of the technology, which limits its adoption in resource-constrained settings. In this paper, we investigate an alternative source of physiological measurement modalities that can track DoA sufficiently while being much more affordable. Thus, we investigate this notion with the use of the University of Queensland database, which comprises EEG-EMG-ECG physiological data from patients going through a variety of surgical procedures. As part of this, select patient datasets were utilized in addition to a variety of signal decomposition and machine learning models—which totaled around 200 simulations—in order to investigate the most optimal combination of algorithms to track DoA using different physiological measurement modalities. The results showed that under certain algorithmic combinations and modeling processes, the ECG measurement (a ubiquitous monitor in anesthetic practice) can rival and occasionally surpass the accuracy of the EEG for DoA monitoring. In addition to this, we also propose a 2-phase modeling process that involves an algorithmic selection stage followed by a model deployment stage. Subsequent work in this area is advised to involve the acquisition of more physiological data from a broader mix of patients in order to further validate the consistency of the findings made in this study.

This research aims to examine the use of image processing and texture analysis to find a more reliable and efficient solution for identifying and classifying types of meat, based on their texture. The method used involves the use of feature extraction, Haar wavelet, and gray-level co-occurrence matrix (GLCM) (with angles of 0°, 45°, 90°, and 135°), supported by contrast, correlation, energy, homogeneity, and entropy matrices. The test results showed that the k-NN algorithm excelled at identifying the texture of fresh (99%), frozen (99%), and rotten (96%) meat, with high accuracy. The GLCM provided good results, especially on texture images of fresh (183.21) and rotten meat (115.79). The Haar wavelet results were lower than those of the k-NN algorithm and GLCM, but this method was still useful for identifying texture images of fresh meat (89.96). This research development is expected to significantly increase accuracy and efficiency in identifying and classifying types of meat based on texture in the future, reducing human error and aiding in prompt evaluation.

Wind power is increasingly seen as a global, sustainable, and eco-friendly energy option. However, one significant obstacle to further wind energy investment is the high failure rate of wind turbines. The gearbox plays a pivotal role in turbine performance. In recent years, there has been a surge in the focus on gearbox fault diagnosis, reflecting its criticality and prevalence in the industry. Time synchronous averaging (TSA) is a primary technique to identify faults in wind turbine gearboxes using mechanical vibration signals. Generally, implementing TSA requires a device that is capable of recording the phase information of a rotary shaft. Nevertheless, there are situations in which the installation of such a device poses difficulties. For instance, gearboxes that are in use cannot be halted to allow for the installation of a device, and sealed gearboxes provide challenges while being inserted into the device. This research presents an innovative technical way to improve the TSA method without requiring a phase signal. The proposed method has the advantage of extracting the shaft rotation angle signal from the measured acceleration signal, even in non-stationary conditions where the rotational speed varies over time. The effectiveness of the proposed method is validated through measured datasets from wind turbine gearboxes with actual faults and a dataset from a gear system with variable rotational speeds.

Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars province, located in southern Iran. The main purpose of this research is to investigate the capabilities of the PRISMA satellite in estimating SOC and examine hyperspectral processing techniques for improving SOC estimation accuracy. To this end, denoising methods and a feature generation strategy have been used. For denoising, three distinct algorithms were employed over the PRISMA image, including Savitzky–Golay + first-order derivative (SG + FOD), VisuShrink, and total variation (TV), and their impact on SOC estimation was compared in four different methods: Method One (reflectance bands without denoising, shown as M#1), Method Two (denoised with SG + FOD, shown as M#2), Method Three (denoised with VisuShrink, shown as M#3), and Method Four (denoised with TV, shown as M#4). Based on the results, the best denoising algorithm was TV (Method Four or M#4), which increased the estimation accuracy by about 27% (from 40% to 67%). After TV, the VisuShrink and SG + FOD algorithms improved the accuracy by about 23% and 18%, respectively. In addition to denoising, a new feature generation strategy was proposed to enhance accuracy further. This strategy comprised two main steps: first, estimating the number of endmembers using the Harsanyi–Farrand–Chang (HFC) algorithm, and second, employing Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformations to generate high-level features based on the estimated number of endmembers from the HFC algorithm. The feature generation strategy was unfolded in three scenarios to compare the ability of PCA and ICA transformation features: Scenario One (without adding any extra features, shown as S#1), Scenario Two (incorporating PCA features, shown as S#2), and Scenario Three (incorporating ICA features, shown as S#3). Each of these three scenarios was repeated for each denoising method (M#1–4). After feature generation, high-level features were added to the outputs of Methods One, Three, and Four. Subsequently, three machine learning algorithms (LightGBM, GBRT, RF) were employed for SOC modeling. The results showcased the highest accuracy when features obtained from PCA transformation were added to the results from the TV algorithm (Method Four—Scenario Two or M#4–S#2), yielding an R2 of 81.74%. Overall, denoising and feature generation methods significantly enhanced SOC estimation accuracy, escalating it from approximately 40% (M#1–S#1) to 82% (M#4–S#2). This underscores the remarkable potential of hyperspectral sensors in SOC studies.

This paper deals with construction and studying wavelet type Bernstein operators by using the compactly supported Daubechies wavelets of the given function $f$. The basis used in this construction is the wavelet expansion of the function $f$ instead of its rational sampling values $f\big( \frac{k}{n}\big)$. After that, we investigate some properties of these operators in some function spaces.

Background and Objectives: The problem of representation of multidimensional data on a two-dimensional plane arises during the processing of a series of two-dimensional images in the spatiotemporal and time-frequency domains. When implementing time-frequency analysis, each point of the object is characterized by a function of two arguments, therefore, to visualize the results on a two-dimensional plane, it is necessary to reduce the data dimension. Materials and Methods: This paper describes a method for color coding the correlation of spectral features at each point of a two-dimensional dynamic image. The novelty of the proposed method in the using of the wavelet correlation function of the reference area with all other regions of interest of the object. In this case, the correlation value is color-coded and forms a correlation map in each of the analyzed spectral ranges. Results: This allows to select areas that have similar time-frequency spectra of investigated characteristics of the object. The application of the method is considered on the example of the analysis of the microhemodynamics of the human hand using photoplethysmographic imaging. The analysis was carried out in the spectral range (0.005–2 Hz), covering both cardiac and low-frequency hemodynamic oscillations of the respiratory, myogenic, neurogenic, and endothelial ranges. In general, there is a tendency to a decrease of correlation of the spectrum with distance from the reference area and with a decrease in the analyzed signal frequency. It is shown that photoplethysmographic signals recorded in the area of the distal phalanx of the finger are predominantly representative of cardiac oscillations in microhemodynamics of other areas of the hand (correlation of about 0.7) and less representative with respect to endothelial, neurogenic, myogenic and respiratory oscillations (correlation of about 0.4). Due to the established high spatial inhomogeneity of the spectral features, it is recommended to use several reference areas when using contact photoplethysmographic measurements. Conclusion: The considered method of visualizing the spatial correlation of spectral features can find practical application also in the field of hemodynamic analysis using laser Doppler, laser speckle contrast, thermographic or hyperspectral imaging.

Abstract To measure the diversification capability of Bitcoin, this study employs wavelet analysis to investigate the coherence of Bitcoin price with the equity markets of both the emerging and developed economies, considering the COVID-19 pandemic and the recent Russia-Ukraine war. The results based on the data from January 9, 2014 to May 31, 2022 reveal that compared with gold, Bitcoin consistently provides diversification opportunities with all six representative market indices examined, specifically under the normal market condition. In particular, for short-term horizons, Bitcoin shows favorably low correlation with each index for all years, whereas exception is observed for gold. In addition, diversification between Bitcoin and gold is demonstrated as well, mainly for short-term investments. However, the diversification benefit is conditional for both Bitcoin and gold under the recent pandemic and war crises. The findings remind investors and portfolio managers planning to incorporate Bitcoin into their portfolios as a diversification tool to be aware of the global geopolitical conditions and other uncertainty in considering their investment tools and durations.

Early detection of plant diseases is crucial for safeguarding crop yield, especially in regions vulnerable to food insecurity, such as Sub-Saharan Africa. One of the significant contributors to maize crop yield loss is the Northern Leaf Blight (NLB), which traditionally takes 14–21 days to visually manifest on maize. This study introduces a novel approach for detecting NLB as early as 4–5 days using Internet of Things (IoT) sensors, which can identify the disease before any visual symptoms appear. Utilizing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) models, nonvisual measurements of Total Volatile Organic Compounds (VOCs) and ultrasound emissions from maize plants were captured and analyzed. A controlled experiment was conducted on four maize varieties, and the data obtained were used to develop and validate a hybrid CNN-LSTM model for VOC classification and an LSTM model for ultrasound anomaly detection. The hybrid CNN-LSTM model, enhanced with wavelet data preprocessing, achieved an F1 score of 0.96 and an Area under the ROC Curve (AUC) of 1.00. In contrast, the LSTM model exhibited an impressive 99.98% accuracy in identifying anomalies in ultrasound emissions. Our findings underscore the potential of IoT sensors in early disease detection, paving the way for innovative disease prevention strategies in agriculture. Future work will focus on optimizing the models for IoT device deployment, incorporating chatbot technology, and more sensor data will be incorporated for improved accuracy and evaluation of the models in a field environment.

AbstractThis study explores the nexus among government revenue, government expenditure, and gross domestic product (GDP) across time and/or frequency using a Ghana dataset. It applies the wavelet approaches to investigate the lead-lag nexus, degree of integration and interdependency among public expenditure, public revenue, and gross domestic product (GDP) in the Ghanaian context. The data source is the World Bank and consists of yearly data from 1983 to 2021 yielding 39 observations. While public expenditure and revenue are closely related, and mostly positive throughout both time and frequency, there is a nexus between public spending and revenue at specific intervals and on a regular basis that was bi-directional, bi-causal, and interdependent. Between 1993 and 2021, a bi-directional nexus could be seen; however, most of the arrows pointed to the right. This shows that the co-movements between public spending and revenue are positive and behave similarly as a result. Accordingly, it is anticipated that increased levels of public spending will be met by higher levels of public spending, and vice versa. This is the first application of a wavelet approach to the study of this phenomenon with a Ghana dataset.

Rainfall characteristics are changing due to several reasons and change/trend detection is required. Literature survey reveals many relevant studies whose outcomes are divergent, possibly because different data series and different methodologies have been applied. This paper presents a critical appraisal of past studies and methodologies for trend analysis. Results of trend analysis of Indian rainfall data are presented. Data for all of India and for five homogenous regions (North-West, Central North-East, North-East, West Central, and Peninsular India) for 1871–2016 were used. The Pettitt change point test, regression, Mann-Kendall (MK), and Wavelet Decomposition were used to study different aspects of changes. Results of the change point test showed that most rainfall series had change points around 1957–65, possibly due to large-scale land use, cultivation, irrigation, and industrial changes in this period. Generally, rainfall for most homogenous regions and sub-divisions show falling trends; some are statistically significant. Series was also decomposed by the wavelet method. Approximate and detailed components of some decomposed series showed a significant declining trend. This work has focused on the magnitude of rainfalls; trends in rainfall intensities are also important. It is necessary to establish denser observation networks to collect short-term data and analyze. HIGHLIGHTS Detection of trends in data series helps in projections.; We present the results of trend analysis using long-term quality-controlled rainfall data.; Data series at country and regional levels at annual and monsoon scales were studied.; We found that most rainfall data have change points around 1957–1965; some are statistically significant.; Multi-resolution analysis highlighted periods of high variabilities in the data.;

Abstract Introduction: Drought is one of the natural hazards that have random and nonlinear behavior due to its various climatic parameters. SPI index is the most common index extracted from rainfall that has been used in modeling drought by various researchers. Methods: The use of computational intelligence methods to model drought in recent years has been much considered by researchers in the field of water resources. In this research, SVR and GPR algorithms individually and also the combination of these algorithms with wavelet algorithms have been modeled and predicted by SPI index, and the purpose was to evaluate the improvement of computational intelligence algorithms in combination with wavelet. In this research, the time series data of 10 synoptic stations in Iran in the period 1961 to 2017 have been used on a monthly basis for modeling the drought as the input of the studied algorithms. Findings: The results of this study showed that the use of the wavelet method in combination with SVR and GPR computational intelligence algorithms improved the results in all time scales. Also, the modeling improvement is due to the use of wavelet in combination with the SVR model with an average RMSE difference of -0.1540 and R2 difference of 0.1491 and the GPR model with an average RMSE difference of -0.1554 and R2 difference of 0.1530 Compared to the single SVR and GPR models showed that the GPR model in general (all time scales and all stations) had a better improvement in the hybrid model than the single model.

Abstract Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery. Therefore, it is difficult to extract, analyze, and diagnose mechanical fault features. To accurately extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery, a study on the time-frequency feature extraction method of multi-source shock signals is conducted. Combining the characteristics of reciprocating mechanical vibration signals, a targeted optimization method considering the variational modal decomposition (VMD) mode number and second penalty factor is proposed, which completed the adaptive decomposition of coupled signals. Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals, a new bilateral adaptive Laplace wavelet (BALW) is established. A search strategy for wavelet local parameters of multi-shock signals is proposed using the harmony search (HS) method. A multi-source shock simulation signal is established, and actual data on the valve fault are obtained through diesel engine fault experiments. The fault recognition rate of the intake and exhaust valve clearance is above 90% and the extraction accuracy of the shock start position is improved by 10°.

This paper presents the results of the analysis of existing methods for clustering data obtained during endoscopy of a larynx. A modification of the Viola-Jones method for image recognition using the flexible exit criterion is proposed. The Viola-Jones method explores all areas in the image and decides whether the recognized area belongs to the desired one by passing through a classified cascade. Endoscopic images have a large number of features, such as flare, noise, etc., which degrade the quality of recognition. To improve the quality of recognition, clustering with a flexible exit criterion was proposed, which satisfies the scalability criteria: changing the decision of the solution, instead of moving to another recognition area. It has been established that the proposed modification of the Viola-Jones method shows higher recognition results for endoscopic images.

Automatic speech recognition systems are developed for translating the speech signals into the corresponding text representation. This translation is used in a variety of applications like voice enabled commands, assistive devices and bots, etc. There is a significant lack of efficient technology for Indian languages. In this paper, an wavelet transformer for automatic speech recognition (WTASR) of Indian language is proposed. The speech signals suffer from the problem of high and low frequency over different times due to variation in speech of the speaker. Thus, wavelets enable the network to analyze the signal in multiscale. The wavelet decomposition of the signal is fed in the network for generating the text. The transformer network comprises an encoder decoder system for speech translation. The model is trained on Indian language dataset for translation of speech into corresponding text. The proposed method is compared with other state of the art methods. The results show that the proposed WTASR has a low word error rate and can be used for effective speech recognition for Indian language.

Wavelet transform is a powerful time-frequency-based analysis method often used in gear fault diagnostics. The development of wavelet transform is closely linked to the improvement of resolution. When the high-frequency resolution allows for easy observation of different frequency components, it is a symptom of damage to an individual part of the machine. This study effectively applied the Wavelet analysis technique to diagnose faulty gearboxes operated in non-stationary conditions. This is a complex problem that usual diagnostic approaches need help to solve due to its non-linear character. This work conducted a simulation and real-world testing to show that the newest wavelet analysis techniques work well, showing that they can accurately find gear faults in gearboxes in non-stationary conditions. A thorough overview of the cutting-edge applications of wavelet transform in diagnosing faults in industrial gearbox systems was also given. This work also explained in detail the mathematical ideas behind the continuous wavelet transform, discrete wavelet transforms, and wavelet packet transform.

Highway bridges are crucial civil constructions for the transport infrastructure, which require proper attention from the corresponding institutions of each country and constant financing for their adequate maintenance; this is important because different types of damage can be generated within these structures, caused by natural disasters, among other sources, and the heavy loads they transport every day. Therefore, the development of simple, efficient, and low-cost methods is of vital importance, allowing us to identify damage in a timely manner and avoid bridges collapsing. As reported in a previous work, the wavelet energy accumulation method (WEAM) and its corresponding application in the Rio Papaloapan Bridge (RPB) represented an important advance within the field. Despite identifying damage in bridges with precision and at a low cost, there are several aspects to improve in that method. Therefore, in this work, that method was improved, eliminating several steps, and meaningfully reducing the computational burden by implementing an algorithm based on the Shannon entropy, thus giving way to the new entropy wavelet-based method (EWM). This new method was applied directly with regard to the real-life RPB, in both its healthy and damaged conditions. Also, its corresponding numerical model based on the finite element method in its healthy condition and different damage scenarios were carried out. The results indicate that the new EWM retains the advantages of WEAM, and it allows for damage identification to be completed more efficiently, increasing the precision by approximately 0.11%, and significantly reducing the computing time required to obtain results by 5.67 times.

The aim of such work is to design a Meyer wavelet neural network (WNN) for solving the mathematical form of the Leptospirosis disease model (LDM). The global and local search optimization schemes based on the genetic algorithm (GA) and active-set algorithm (ASA) are used in this study. Leptospirosis is an infection spread by rodents, which is found in the world and causes fatalities in humans. The mathematical LDM model form consists of susceptible-infected-recovered (SIR), which is based on the disease spread processes. A fitness function is designed by using the mathematical LMD and then optimized by the hybridization of the GAASA. For the correctness, and capability of the Meyer WNN along with the procedures of GAASA, the comparison of the obtained and reference results is provided. Moreover, the reducible absolute error provides the efficiency of the proposed Meyer WNN along with the procedures of GAASA. The statistical observations are also provided to authenticate the convergence of the stochastic Meyer WNN along with the procedures of GAASA.

Three-phase induction motors (IMs) are considered an essential part of electromechanical systems. Despite the fact that IMs operate efficiently under harsh environments, there are many cases where they indicate deterioration. A crucial type of fault that must be diagnosed early is stator winding faults as a consequence of short circuits. Motor current signature analysis is a promising method for the failure diagnosis of power systems. Wavelets are ideal for both time- and frequency-domain analyses of the electrical current of nonstationary signals. In this paper, the signal data are obtained from simulations of an induction motor for various stator winding fault conditions and one normal operating condition. Our main contribution is the presentation of a fault diagnostic system based on a hybrid discrete wavelet–CNN method. First, the time series of the currents are processed with discrete wavelet analysis. In this way, the harmonic frequencies of the faults are successfully captured, and features can be extracted that comprise valuable information. Next, the features are fed into a convolutional neural network (CNN) model that achieves competitive accuracy and needs significantly reduced training time. The motivations for integrating CNNs into wavelet analysis results for fault diagnosis are as follows: (1) the monitoring is automated, as no human operators are needed to examine the results; (2) deep learning algorithms have the potential to identify even more indistinguishable and complex faults than those that human eyes could.

A method for detecting coal flow on a belt conveyor based on the two-dimensional laser is proposed in this paper to tackle the complications of poor accuracy and real-time detection of coal flow on a belt conveyor. This method collects the point cloud data of the conveyor belt surface and the lateral profile of the coal flow using a two-position laser probe and transmits the data with the industrial Internet to assure the real-time and reliability of the data transmission. The upper computer software of the detection system is designed using LabVIEW software. The improved wavelet threshold algorithm designed by MATLAB is employed for data preprocessing to enhance measurement accuracy. The results demonstrate that the method can accurately detect the coal flow of the belt conveyor, and the detection accuracy reaches more than 95%, presenting the advantages of real-time performance and high accuracy. It has broad application prospects in coal, mines, ports, electric power, and other fields.

Wavelet pooling (WP) in neural network architectures has recently demonstrated more discriminative power than traditional pooling methods. This is mainly because the latter suffer from spatial information loss while wavelet pooling harnesses the power of spectral information. However, the potential of WP in increasing the data efficiency and the extent of this potential have not been investigated yet. Data efficiency refers to the volume of training data required to attain a certain performance level during inference, e.g., recognition accuracy. In this research, we are concerned with evaluating the data efficiency of WP in light-weight architectures–MobileNets. Across a wide variety of seven datasets/applications including object recognition (CIFAR-10, STL-10, CINIC-10, and Intel Image Classification datasets) and diagnostic imaging (colon diseases, brain tumors, and malaria cell images datasets), and while considering classification accuracy as a performance metric, we show that WP achieves an average data saving that exceeds 30% compared to traditional pooling techniques. For other performance measures, namely, precision, recall, and F1-score, we report an average of 30% data saving for object recognition datasets and 22% saving for diagnostic imaging datasets. By focusing on a light-weight architecture, this research further emphasizes the significance of wavelet pooling in training and testing resources-challenged settings such as the applications of edge computing and green deep learning.

Purpose – The purpose of this paper is to examine the effect of economic policy uncertainty (EPU) of China on investment opportunities in five ASEAN economies. Design/methodology/approach – This paper employs advanced empirical approaches, such as Multivariate DCC-GARCH and Continuous Wavelet Transform (CWT) to test the research objective. The period of analysis involved monthly data from 2003 until 2019. Findings – This paper provides evidence where the Malaysian stock market to be the least exposed to risks emanating from Chinese EPU, followed by Singapore, the Philippines, Thailand and Indonesia. Results for investment opportunities based on time horizon suggest, for a short-term holding period, investors are better off investing in Singapore and Indonesia, while, for medium-term holding periods, all ASEAN markets appear lucrative except for the Philippines. Practical implications – From a managerial perspective, the outcome or findings of this study are expected to aid the retail and institutional investors in designing better strategies on diversifying a stock portfolio with different holding periods. Originality/value – Theoretically, the findings of this study contribute fresh insights into an emerging strand of literature focusing on the transmission of regional policy. Methodologically as well, this study is a novel venture to the best of authors' knowledge.

It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an increase in the use of machine learning approaches to model rainfall-runoff. In this study, the accuracy of rainfall-runoff modeling approaches such as support vector machine (SVM), gene expression programming (GEP), wavelet-SVM (WSVM), and wavelet-GEP (WGEP) is evaluated. Python is used to run the simulation. The research area is the Yellow River Basin in central China, and in the west of the region, the Tang-Nai-Hai hydrometric station has been selected. The train state data ranges from 1950 to 2000, while the test state data ranges from 2000 to 2020. The analysis looks at two different types of rainy and non-rainy days. The WGEP simulation performed best, with a Nash-Sutcliffe efficiency (NSE) of 0.98, while the WSVM, GEP, and SVM simulations performed poorly, with NSEs of 0.94, 0.89, and 0.77, respectively. As a result, combining hybrid methods with wavelet improved simulation accuracy, which is now the highest for the WGEP method. HIGHLIGHTS The accuracy of machine learning based approaches for rainfall-runoff modeling is evaluated in the Yellow River Basin in China.; Wavelet transform as a pre-processing tool is combined with Gene Expression Programming and Support Vector Machine.; The combination of Wavelet and Gene Expression Programming (WGEP) performed best.;

In this research, a simple experiment in the field of agriculture was studied, in terms of the effect of out-of-control noise as a result of several reasons, including the effect of environmental conditions on the observations of agricultural experiments, through the use of Discrete Wavelet transformation, specifically (The Coiflets transform of wavelength 1 to 2 and the Daubechies transform of wavelength 2 To 3) based on two levels of transform (J-4) and (J-5), and applying the hard threshold rules, soft and non-negative, and comparing the wavelet transformation methods using real data for an experiment with a size of 26 observations. The application was carried out through a program in the language of MATLAB. The researcher concluded that using the wavelet transform with the Suggested threshold reduced the noise of observations through the comparison criteria.

River streamflow is an essential hydrological parameters for optimal water resource management. This study investigates models used to estimate monthly time-series river streamflow data at two hydrological stations in the USA (Heise and Irwin on Snake River, Idaho). Five diverse types of machine learning (ML) model were tested, support vector machine-radial basis function (SVM-RBF), SVM-Polynomial (SVM-Poly), decision tree (DT), gradient boosting (GB), random forest (RF), and long short-term memory (LSTM). These were trained and tested alongside a conventional multiple linear regression (MLR). To improve the estimation and model performance, hybrid models were designed by coupling the models with wavelet theory (W). The models performance was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), and Willmott’s index (WI). A side-by-side performance assessment of the stand-alone and hybrid models revealed that the coupled models exhibit better estimates of monthly river streamflow relative to the stand-alone ones. The statistical parameter values for the best model (W-LSTM4) during the test phase was RMSE = 36.533 m3/s, MAE = 26.912 m3/s, R2 = 0.947, NSE = 0.946, WI = 0.986 (Heise station), and RMSE = 33.378 m3/s, MAE = 24.562 m3/s, R2 = 0.952, NSE = 0.951, WI = 0.987 (Irwin station).

The electrocardiogram (ECG) is immensely beneficial for diagnosing the arrhythmias that may lead to serious complications in the heart health. In this paper, the continuous wavelet transform (CWT) was used for electrocardiogram arrhythmias detection. The natural arrhythmias were: Supra-ventricular arrhythmias (SV), atrioventricular (AV) and Normocardia (NC) were chosen for detection as well as for testing the proposed method The Natural signals were taken from MIT-BIH database to be used for testing. The continuous wavelet transform was connected to the standard deviation (SD) and Shannon entropy (SE) for feature extraction stage. For classification a safe threshold has been suggested to discriminate between the different arrhythmias. Several combinations of the CWT application were testing. The wavelet packet transform was used for comparison. All combinations have given reasonable results, but continuous wavelet transform with standard deviation taken for the third sub signal have given the superior results. The results of our study will open the door for choosing the continuous transform for detection that has been neglected by the researchers for this task.

The study analyses the asymmetric and nonlinear influence of oil price on exchange rates of South Asian countries in time–frequency framework using wavelet technique. For empirical analysis, monthly data are examined from July 1983 to June 2018. Wavelet coherence results show that the variables are in phase, i.e., oil prices and exchange rates are positively correlated. It suggests that oil price influences exchange rates positively. The study also investigates the causal association between oil price and exchange rates using a nonlinear causality test. The results of nonlinear causality show that there is bidirectional causality between oil price and exchange in Bangladesh and India and unidirectional causality from oil price to exchange rate in Pakistan and Sri Lanka. The findings provide some important recommendations to investors and policy makers.

The theory of applying wavelet neural networks to predict the time series of short-term wind power has important practical significance for the construction of emergency response systems in smart grids. Based on obtaining the low-frequency approximate part and high-frequency random part of wind power data through wavelet decomposition and reconstruction, and analyzing the advantages and disadvantages of various models, a short-term wind power prediction model is established. The simulation of the model realizes the complex mapping from the relevant historical wind power to the prediction target. The prediction results obtained from the measured wind power data of a wind farm show that the model has a fast prediction speed and high accuracy. It can effectively improve the prediction accuracy of short-term wind power.