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Pulse‐like ground motion can cause extreme damage to long‐period structures. An automatic algorithm is proposed to identify pulse‐like ground motions, in which improved anomaly detection is applied and the structural performance is considered. To characterize the intrinsic pulse‐like features, the distance‐based anomaly detection algorithm is improved, and the relative cumulative energy is added to the Manhattan distance. The first application of the anomaly detection algorithm performs well in pulse‐like motion identification. The pulse‐like ground motions are identified according to the performance of typical long‐period engineering structures in Hazus by the improved capacity spectrum method. The method is applied to 214 strong ground motions in the Next Generation Attenuation West 2 database. Comprehensive comparisons of the proposed approach with the wavelet analysis and the energy method are presented to illustrate the significance of the structural performance on the pulse‐like identification method. [ABSTRACT FROM AUTHOR]

Located at the forefront of the collision between the Indian and Eurasian Plates, the Tibetan Plateau experiences intense crustal movement. Traditional ground-based geodetic monitoring, such as GNSS and leveling, is challenging, due to factors such as high altitude and harsh climate, making it difficult to accurately determine a high-resolution crustal deformation field of the plateau. Unaffected by ground observation conditions, InSAR technique has key advantages for obtaining extensive and high-resolution crustal deformation fields. This makes it indispensable for crustal deformation monitoring on the Tibetan Plateau. This study used Sentinel-1 data from 2014 to 2020 to compute the ascending and descending InSAR deformation fields for the Tibetan Plateau. This was conducted with a measurement accuracy of approximately 3 mm/yr. Building upon this, we integrated InSAR and GNSS data to yield kilometer-resolution three-dimensional (3D) crustal deformation and strain rate fields for the Tibetan Plateau. A spherical wavelet analysis was used to decompose the 3D deformation field and separate the non-tectonic crustal deformation to increase the strength of the tectonic deformation signal. Short-wavelength (<110 km) deformations match the distribution of fault movement, post-seismic deformations, and other non-tectonic factors. Long wavelength (>110 km) deformation mainly results from subsidence in the central plateau and uplifts along the Himalayan Arc. This indicates that the Tibetan Plateau may have stopped the entire uplift and entered a local collapse stage. Furthermore, the deformation fields at different spatial scales reveal that the plateau exhibits discontinuous deformation in short wavelengths and continuous deformation in long wavelengths. The findings of this study contribute to resolving the controversy between the Block and Continuum Deformation models of the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

The eradication of gender discrimination traditions and the reduction of income disparities are vital global objectives outlined in Sustainable Development Goals 4 and 10, particularly in African nations where gender and income gaps persist. In alignment with these goals, this study employs wavelet techniques to investigate the relationship between income inequality and gender equality in 31 selected African countries, utilizing available data spanning from 1998 to 2020. Analysis of the co-movement between gender equality and income gap reveals that gender equality plays a crucial role in narrowing the income disparity in African countries. Expanding the analysis to control for the impact of economic growth through the implementation of partial wavelet tools, does not change these results. Further dissemination of our findings reveals that improved gender equality has a more pronounced impact on reducing income inequality in anglophone nations compared to their francophone counterparts, given variations in legal frameworks and religious practices. This emphasizes the importance of gender-sensitive policies in effectively addressing income inequality in Africa and suggests that policymakers should tailor country-specific policies to account for differences in gender laws, cultures, and religious beliefs. Overall, our study presents novel evidence linking income and gender inequality in Africa at country level. [ABSTRACT FROM AUTHOR]

Kernel methods are often used for nonlinear regression and classification in statistics and machine learning because they are computationally cheap and accurate. The wavelet kernel functions based on wavelet analysis can efficiently approximate any nonlinear functions. In this article, we construct a novel wavelet kernel function in terms of random wavelet bases and define a linear vector space that captures nonlinear structures in reproducing kernel Hilbert spaces (RKHS). Based on the wavelet transform, the data are mapped into a low-dimensional randomized feature space and convert kernel function into operations of a linear machine. We then propose a new Bayesian approximate kernel model with the random wavelet expansion and use the Gibbs sampler to compute the model's parameters. Finally, some simulation studies and two real datasets analyses are carried out to demonstrate that the proposed method displays good stability, prediction performance compared to some other existing methods. [ABSTRACT FROM AUTHOR]

According to studies by the Intergovernmental Panel on Climate Change (IPCC), tropical mountainous areas are experiencing increasingly unfavorable climatic conditions regarding geohazards due to a heightened occurrence of intense rainfall events. These climatic shifts contribute to heightened geological risks, notably an elevated frequency of landslides, exacerbating the challenges faced by these regions. There is an urgent need to understand and measure how rainfall variability affects geo-hydrological hazards, which remain difficult to determine and predict. The complex and non-linear space-time relationships and dynamics of rainfall, El Niño-Southern Oscillation (ENSO), and landslides in the Tropical Andean Mountain region in Colombia require an adequate analysis and understanding of their link in terms of its spatial and temporal component at different scales. By evaluation in annual, seasonal, and monthly scales, additional insights on the relationships using a wavelet spectral analysis and a space-time permutation scan statistics method using SaTScan™ are provided. In this study, a space-time and frequency analysis of landslides using a 42-year (1981–2022) rainfall and Multivariate ENSO Index v2.0 time series is presented. According to the results, landslides closely aligned with rainfall patterns, exhibiting a bimodal annual cycle. The ENSO added complexity, with La Niña years leading to more frequent landslides throughout the year and El Niño years showing concentrated occurrences in specific months. This study highlights the influence of rainfall patterns and antecedent rainfall on landslide occurrence, the impact of ENSO phases on rainfall and landslides, and the increasing trend of landslides in Colombia. [ABSTRACT FROM AUTHOR]

Suspended sediment load (SSL) and discharge are closely related concepts that characterise the sediment movement and quantity within water bodies and play a crucial role in evaluating the impact of erosion, sedimentation, and land management practices on riverine ecosystems. Despite numerous studies on SSL and discharge assessments, a site-specific understanding remains essential for effective riverine ecosystem protection. This study integrates daily sediment and discharge data from the Koelwar station spanning June 2010 to May 2015, with a primary focus on analysing SSL variability relative to Sone River discharge in Bihar. SSL–discharge rating curves were computed to assess concentration shifts during low flow season (LFS), high flow season (HFS), and the annual period. Results indicate a nearly linear dependence of SSL on discharge, with r2 values of 0.72, 0.94, and 0.84 for LFS, HFS, and the annual period, respectively. The study unveils the underlying mechanics driving catchment erosion and sediment transport during lean and peak flows through hysteresis loops. Findings reveal that 50% of events are influenced by shear stress and hydraulic forces from the channel bed, 20% by sediment depletion in the channel bed due to repeated peak flows, and the remaining 30% by a combination of shear stress and sediment reduction. Additionally, MATLAB-utilized wavelet analysis explains the time-frequency variability of discharge and SSL. Cross-wavelet analysis shows the outflow of SSL closely following discharge, indicating synchronisation across all scales. The study underscores the importance of effectively managing runoff events to mitigate SSL. The proposed methodology and results offer valuable insights for water management and policymakers, aiding in the formulation of guidelines to minimise anthropogenic activities and sustain aquatic ecosystems. [ABSTRACT FROM AUTHOR]

In this study, wavelet-based damage-sensitive features are employed to derive the seismic fragility functions/curves for reinforced concrete moment-resisting frames. Two different wavelet transform functions, namely, Bior3.3 and Morlet mother wavelet families, were applied to absolute acceleration time histories of building frames to extract the wavelet-based and refined wavelet-based damage-sensitive features (i.e., DSF and rDSF). The accuracy of seismic assessments and certainty in predicting structural behavior strongly depend on the specific optimal intensity measures selected, reliability of wavelet-based damage-sensitive features, and some such intensity measures as PGA, PGV, PGD, Sa, and Sdi as the conventionally utilized measures to detect the damage state of a structure. These measures were examined against their statistical properties of efficiency, practicality, proficiency, coefficient of determination, and sufficiency to select the appropriate optimal intensity measures, which were then used to drive the fragility curves disclosing the failure or other damage states of interest. For the purposes of this study, three different concrete moment-resisting frames with four-, eight-, and twelve-story building frames were adopted for implementing the proposed approach. The findings demonstrate that the wavelet-based damage-sensitive features (DSFs/rDSF) simultaneously satisfy all the statistical properties cited above. This is evidenced by the low variance and dispersions observed in the frame damage state predictions by the fragility functions derived from the wavelet-based DSF when compared with those derived from the classical fragility analyses such as spectral acceleration at the first mode period of the structure. A final aspect of the study concerns the superior performance and efficiency of the fragility curves derived by the Bior3.3 wavelet-based DSF over those derived from Morlet wavelet-based DSF. [ABSTRACT FROM AUTHOR]

The manuscript presents a pilot study of the impact of orthodontic intervention on the brain electrical activity. The orthodontic treatment is a powerful factor of both physiological influence on the jaw system and the surrounding tissues of the head and stress influence. All practically healthy subjects of the same age category (18–25 years) were distributed among three groups based on the method of orthodontic treatment. Group 1 included patients using braces, groups 2 and 3 included patients using aligners in which pressure was applied to 3–5 or 1–2 teeth, respectively. Brain activity electroencephalographic data were collected twice during neurophysiological monitoring: before and after orthodontic correction. The collected data sets included EEG signals from the occipital region of the brain. Numerical processing was performed based on continuous wavelet analysis to estimate the number and duration of oscillatory patterns in narrow frequency bands from 1 to 50 Hz. An assessment of the oscillatory brain activity demonstrated that different grades of correction intensity, regarding the dentition and occlusion, lead to uniform changes in the oscillatory patterns assessed by the electroencephalography in the occipital lobe. Comparison of the number of oscillatory patterns in the groups showed significant changes in the high-frequency ⋃ HF ∋ 16 ; 18 , 20 ; 28 , 32 ; 34 , 42 ; 50 Hz. The number of patterns in the ⋃ HF -band increases when using the most intense bracket devices; while in cases of more gentle correction based on aligner systems, it remains unchanged or even decreases. The independent clustering procedure by assessing changes in oscillatory processes of ⋃ HF -band occurring in a single occipital O1-canal made it possible to divide the data array into three clusters. The clusters of changes in brain activity correspond to clinical groups of patients. Thus, different types of dental exposure lead to significantly different changes in the brain activity of patients. [ABSTRACT FROM AUTHOR]

We performed a wavelet analysis of oscillatory dynamics in brain activity of patients with obstructive sleep apnoea (OSA) ( N = 10 , age 52.8 ± 13 years, median 49 years; male/female ratio: 73), compared with a group of apparently healthy participants ( N = 15 , age 51.5 ± 29.5 years, median 42 years; malefemale ratio: 87), based on the calculation of patterns from electroencephalographic (EEG) signals of ***nighttime polysomnography (PSG) recordings. It was shown that there were no statistical differences in the number and duration of nocturnal sleep stages in patients of the two groups. The distributions of the number N and duration T of oscillatory wavelet patterns of EEG signals in bands Δ f i = [ i ; i + 2 ] , where i takes values from 2 to 38, have been estimated. Statistically significant differences in the characteristics of the distributions of the number and duration of patterns for the high-frequency bands Δ f 17 – Δ f 19 (32 – 38 Hz) are shown. It is demonstrated that estimation of the coordinates of the height and the value of the maximum point of the distribution of the considered quantitative characteristics of the patterns allows clustering of the EEG processing results and demonstrates the separation of the nocturnal sleep characteristics of OSA patients and healthy volunteers. Evaluation based on the Mann–Whitney U-test shows statistically significant differences between N and T patterns assessed from nocturnal EEG recordings. The number and duration of high-frequency patterns are significantly reduced in the EEG of OSA patients compared to essentially healthy participants. It is possible that such a change in high-frequency activity is related to known structural changes in the brain. [ABSTRACT FROM AUTHOR]

The Madden–Julian oscillation (MJO) propagates eastward as a disturbance of mostly zonal wind and precipitation along the equator. The initial diagnosis of the MJO spectral peak at 40–50-day periods suggests a reduction in amplitude associated with slower MJO events that occur at lower frequencies. If events on the low-frequency side of the spectral peak continued to grow in amplitude with reduced phase speed, the spectrum would just be red. Wavelet regression analysis of slow and fast eastward-propagating MJO signals during northern winter assesses how associated moisture and wind patterns could explain why slow MJO events achieve lower amplitude in tracers of moist convection. Results suggest that slow MJO events favor a ridge anomaly over Europe, which drives cool dry air equatorward over Africa and Arabia as the active convection develops over the Indian Ocean. We hypothesize that dry air tracing back to this source, together with a longer duration of the events, leads to associated convection diminishing along the equator and instead concentrating in the Rossby gyres off the equator. Significance Statement: The Madden–Julian oscillation (MJO) dominates the subseasonal variability of the tropical atmosphere. This work suggests that it favors maximum convective activity in the 40–50-day period range because lower-frequency MJO signals tend to import more cool dry air from the extratropics and along the equator, thereby weakening the slower events. [ABSTRACT FROM AUTHOR]

The quantitative study of many physical problems ultimately boils down to solving various partial differential equations (PDEs). Wavelet analysis, known as the "mathematical microscope", has been hailed for its excellent Multiresolution Analysis (MRA) capabilities and its basis functions that possess various desirable mathematical qualities such as orthogonality, compact support, low‐pass filtering, and interpolation. These properties make wavelets a powerful tool for efficiently solving these PDEs. Over the past 30 years, numerical methods such as wavelet Galerkin methods, wavelet collocation methods, wavelet finite element methods, and wavelet integral collocation methods have been proposed and successfully applied in the quantitative analysis of various physical problems. This article will start from the fundamental theory of wavelet MRA and provide a brief summary of the advantages and limitations of various numerical methods based on wavelet bases. The objective of this article is to assist researchers in choosing the appropriate numerical methodologies for their particular physical issues. Furthermore, it will explore prospective advancements in wavelet‐based techniques, offering valuable insights for researchers committed to enhancing wavelet numerical methods in the field of computational physics. [ABSTRACT FROM AUTHOR]

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (airborne particles with an aerodynamic diameter of less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley: one in the west/northwest and the other in the southeast. Wavelet analysis revealed the annual cycle to be the most persistent temporal mode of PM10 variability in both subregions, with intermittent signals also observed at time scales of around 120, 30~90, and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere. [ABSTRACT FROM AUTHOR]

Extreme events, such as droughts, are influenced by climate variability modes such as El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multi‐decadal oscillation (AMO) and Equatorial Indian Ocean Oscillation (EQUINOO). Due to the significant interdependency among the climatic indices at various frequencies, a time‐frequency framework is necessary to better understand the teleconnection. The present study evaluates the association between climate variability modes and drought index in India using different variants of wavelet analysis such as wavelet coherence analysis (WCA), partial wavelet coherence analysis (PWCA), multiple wavelet coherence analysis (MWCA) and wavelet reconstruction methods. In this study, four major climatic indices (ENSO, PDO, AMO, EQUINOO) and the drought index, standard precipitation index (SPI), at four different monthly time periods (1, 3, 6 and 12) are considered. The results from the WCA analysis highlight significant teleconnection spectra for ENSO, PDO and EQUINOO at 2–8, 8–16 and 16–32 year time‐frequency bands. Further, PWCA results reveal significant interdependency in the teleconnection of ENSO and PDO at all the scales, while ENSO and EQUINOO are found to be independent modes of interannual drought variability in India. Finally, MWCA results show that the combination of EQUINOO and ENSO better explains the interannual variability of droughts in Northwest and Central northeast India. The results of this study will help improve drought prediction and early warning systems in India, by selection of appropriate climatic oscillations for different regions in India. [ABSTRACT FROM AUTHOR]

Cool‐water habitats provide increasingly vital refuges for cold‐water fish living on the margins of their historical ranges; consequently, efforts to enhance or create cool‐water habitat are becoming a major focus of river restoration practices. However, the effectiveness of restoration projects for providing thermal refuge and creating diverse temperature regimes at the watershed scale remains unclear. In the Klamath River in northern California, the Karuk Tribe Fisheries Program, the Mid‐Klamath Watershed Council, and the U.S. Forest Service constructed a series of off‐channel ponds that recreate floodplain habitat and support juvenile coho salmon (Oncorhynchus kisutch) and steelhead (O. mykiss) along the Klamath River and its tributaries. We instrumented these ponds and applied multivariate autoregressive time series models of fine‐scale temperature data from ponds, tributaries, and the mainstem Klamath River to assess how off‐channel ponds contributed to thermal regime diversity and thermal refuge habitat in the Klamath riverscape. Our analysis demonstrated that ponds provide diverse thermal habitats that are significantly cooler than creek or mainstem river habitats, even during severe drought. Wavelet analysis of long‐term (10 years) temperature data indicated that thermal buffering (i.e. dampening of diel variation) increased over time but was disrupted by drought conditions in 2021. Our analysis demonstrates that in certain situations, human‐made off‐channel ponds can increase thermal diversity in modified riverscapes even during drought conditions, potentially benefiting floodplain‐dependent cold‐water species. Restoration actions that create and maintain thermal regime diversity and thermal refuges will become an essential tool to conserve biodiversity in climate‐sensitive watersheds. [ABSTRACT FROM AUTHOR]

Several state-of-the-art convolutional neural networks (CNNs)-based methods are available for image denoising tasks. CNNs are typically trained using the backpropagation algorithm, which requires all operations in the network to be differentiable. Most CNN operations satisfy this requirement and can be applied to backpropagation-based training algorithms. However, some transforms, including wavelet transform, which is useful for speeding up CNN computations as well as performing multi-resolution analysis, are not strictly differentiable. This paper addresses this challenge by proposing a wavelet-like transform that is differentiable. This new design is, in fact, a new CNN architecture named semi-wavelet, specific edge convolutional neural network (SW/SE-CNN), consisting of three newly designed layers. The first layer is a Semi-Wavelet (SW)-based layer which is a differential down-sampling operator for wavelet approximation. That is, the SW layer converts the input image into four channels. Three of these channels are estimations of the vertical, horizontal, and diagonal edges of the original image; and the fourth channel is a down-sampled version of it. The second proposed layer, called Semi-Wavelet Inverse (SWI), is to restore the original image by using the four SW output channels. Additionally, a specific edge extractor (SE), as another new layer, is designed on the basis of the well-known Sobel operator to extract specific edges of the image. The reason behind proposing the SE layer is to provide more edge information for the network; and the motive for including the SW layer is to speed the network up as well as multi-resolution analysis. Then, the new SW/SE-CNN architecture is implemented for Gaussian image denoising. The experimental results show that the new SW/SE-CNN outperformed the state-of-the-art methods for Gaussian image denoising based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) measurements for grayscale as well as color images. [ABSTRACT FROM AUTHOR]

The spatial response mechanism of hydrology and water quality of large river-connected lakes is very complicated. In this study, we developed a spatial response analysis method that couples wavelet correlation analysis (WTC) with self-organizing maps (SOM), revealing the spatial response and variation of water level and water quality in Poyang Lake, China's largest river-connected lake, over the past decade. The results show that: (1) there was significant spatial heterogeneity in water level and quality during the dry seasons (2010–2018) compared to other hydrological stages. (2) We identified a more pronounced difference in response of water level and quality between northern and southern parts of Poyang Lake. As the distance increases from the northern lake outlet, the impact of rising water levels on water quality deterioration intensified during the dry seasons. (3) The complex spatial heterogeneity of water level and quality response in the dry seasons is primarily influenced by water level fluctuations from the northern region and the cumulative pollutant entering the lake from the south, which particularly leads to the reversal of the response in the central area of Poyang Lake. The results of this study can contribute to scientific decision-making regarding water environment zoning management in large river-connected lakes amidst complex environment conditions. [ABSTRACT FROM AUTHOR]

The dynamics of the parameters of the diffraction peak 0012 of LiNbO3:Fe crystals was recorded with a time resolution of less than 1 ns using synchronization of nanosecond laser pulses with synchrotron bunches of the KISI-Kurchatov source. The impact of a laser pulse (λ = 532 nm, τ = 4 ns, energy density 0.6 J/cm2) at different polarization directions of laser radiation causes a change in the peak intensity; this change depends on the angle between the laser beam polarization direction and the crystallographic axes. The obtained results are supplemented with the wavelet analysis of experimental data. The observed polarization dependence correlates with the data in the literature on the photovoltaic effect. [ABSTRACT FROM AUTHOR]

Based on the daily meteorological observation data of seven meteorological stations in southern Tibet from 1980 to 2021 (April October), the temporal and spatial variation characteristics and influencing factors of aridity index (AI) in the growing season of major grain producing areas in Tibet were studied by using climate tendency rate, Mann-Kendal test, Morlet wavelet analysis, GIS hybrid interpolation method, Pearson correlation coefficient, contribution rate analysis and other methods. The results showed that the average Al in the main grain producing areas of Tibet was 1.7, which belonged to the semi-arid area, and the overall trend was decreasing (humidifying) (-0.036/10 a). The linear decreasing trend was different in different regions, and the area around Lhatse County was the most significant (-0.26/10 a). Al had no obvious abrupt change, and had long- and medium-term fluctuation characteristics of 24 years, 6 years. The spatial dis- tribution was uneven, and had the characteristics of 'shrinking arid area and expanding humid area'. The contribution rates of the main climate influencing factors of Al varied in different regions. In general, the contribution rates after quantification was as follows: precipitation (34.9%) > relative humidity (28.4%) > sun- shine (19.9%) > maximum temperature (12.4%). [ABSTRACT FROM AUTHOR]

In this paper, a Euler wavelets method for solving the variational problems is presented. The operational matrices of integration and product of Euler wavelets are calculated. Then, by using Euler wavelets and the operational matrices, the variational problems are reduced into the system of algebraic equations. Furthermore, the convergence analysis and error bound of the Euler wavelets method are given. Some examples are included to demonstrate the applicability and validity of the schemes. [ABSTRACT FROM AUTHOR]

This research aims to comprehensively examine the clearness index (KT), total ozone column (TOC), and ultraviolet A (UVA) and ultraviolet B radiation (UVB) over Lumbini, Nepal (27°28' N, 83°16' E, and 150 m above sea level) throughout the 11 years of solar cycle 24 (2008 to 2018). The Lumbini, a highly polluted region, is important in advancing the identification and analysis of TOC variations across regions with similar geographical and climatic attributes. Data from the Ozone Monitoring Instrument (OMI) of the EOS-AURA satellite of NASA were used to analyze the daily, monthly, seasonal, and annual trends in the clearness index (KT), ultraviolet A (UVA), ultraviolet B (UVB), and TOC from the Comprehensive Environmental Data Archive (CEDA). The study found that the yearly averages for KT, TOC, UVA, and UVB were 0.55 ± 0.13, 272 ± 14 DU, 12.61 ± 3.50 W/m2, and 0.32 ± 0.11 W/m2, respectively. These values provide insights into the long-term variations in atmospheric parameters at Lumbini. The study also applied the continuous wavelet transform (CWT) to analyze KT, TOC, UVA, and UVB temporal variations. The power density peak of 35,000 DU2 in the TOC was observed from the end of 2010 to the end of 2011, within 8.5 years, underscoring the significance of analyzing TOC dynamics over extended durations to understand atmospheric behavior comprehensively. [ABSTRACT FROM AUTHOR]

In order to expand the detection range and ensure the operation stability, the substation multi-source perception blind compensation detection method based on equipment visual blind area recognition and significance detection is studied. Acoustic sensors are used to collect acoustic signals from visual blind areas of equipment. The characteristics of noise signal are identified by wavelet analysis and noise reduction. The supercomplex Fourier transform model was used to extract the important region in the device image, and the texture features of the region were detected by Gabor filter. The blind compensation detection feature vector is formed by integrating two multi-source sensing features. The detection model of support vector machine is input to complete the blind compensation detection of the substation. The experimental results show that the proposed method is effective for the sound signal feature recognition in the visual blind area and the texture feature detection in the significant area of the device image. The different operating states of each equipment detected by the multi-source sensing feature vector are more accurate, which can realize the purpose of the multi-source sensing blind compensation check of the substation and ensure the safe and stable operation of the substation. [ABSTRACT FROM AUTHOR]

Sensitivity analysis plays a pivotal role for the development and calibration of hydrological models, since they are often affected by equifinality. Despite a lot of effort has been placed for the development of effective sensitivity analysis methods, hydrological models remain over parametrized. We take advantage of the evidence that hydrological processes can be described as the superposition of effects occurring at different temporal scales (e.g., seasonal precipitation patterns, seasonal and daily snow and glacier melt, seasonal, daily and sub-daily water management operations) to develop a new framework to perform sensitivity analysis. We apply discrete and continuous wavelet transforms to disentangle hydrological signals occurring at different temporal scales and we take advantage of the different information stored at different temporal scales of the wavelet spectrum to perform a scale-dependent sensitivity analysis. This approach aims to increase the number of identifiable model parameters in comparison to standard sensitivity analysis performed in the time domain. As an exemplary problem, we apply the methodology to synthetic data describing surface water-groundwater interaction in rivers affected by hydropeaking (i.e., sudden fluctuations in the river stage due to hydropower production). The method could be applied also to other models displaying the superposition of processes with different intensities at different temporal scales such as ocean tide propagation in aquifers as well as snow and glacier melt models. The results indicate that considering multiple temporal scales allows us to increase the number of parameters that can be identified and hence calibrated with only a little increase in the computational effort. [ABSTRACT FROM AUTHOR]

This paper presents an approach based on deep learning for accurate Electrocardiogram signal classification. The electrocardiogram is a significant signal in the realm of medical affairs, which gives vital information about the cardiovascular status of patients to heart specialists. Manually meticulous analysis of signals needs high and specific skills, and it is a time-consuming job too. The existence of noise, the inflexibility of signals, and the irregularity of heartbeats keep heart specialists in trouble. Cardiovascular diseases (CVDs) are the most important factor of fatality globally, which annually caused the deaths of 17.9 million people. Totally 31% of all death in the world are related to CVDs, which the age of 1/3 of patients that died because of CVDs is below 70 Because of the high percentage of mortality in cardiovascular patients, accurate diagnosis of this disease is an important matter. We present an approach to the analysis of electrocardiogram signals based on the convolutional neural network, discrete wavelet transformation with db2 mother wavelet, and synthetic minority over-sampling technique (SMOTE) on the MIT-BIH dataset according to the association for the advancement of medical instrumentation (AAMI) standards to increase the accuracy in electrocardiogram signal classifications. The evaluation results show this approach with 50 epoch training that the time of each epoch is 39 s, achieved 99.71% accuracy for category F, 98.69% accuracy for category N, 99.45% accuracy for category S, 99.33% accuracy for category V and 99.82% accuracy for category Q. It is worth mentioning that it can potentially be used as a clinical auxiliary diagnostic tool. The source code is available at https://gitlab.com/arminshoughi/ecg-classification-cnn. [ABSTRACT FROM AUTHOR]

This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, undergo a scaled continuous wavelet transform (S-CWT) to generate scalogram images. These images are subsequently utilized as inputs for pretrained deep learning models. The experiments encompass various fault scenarios, spanning distinct fault types, locations, times, and resistance values. A remarkable feature of the proposed work is the attainment of 100% classification accuracy, obviating the need for additional algorithmic enhancements. The foundation of this achievement is the deliberate selection of the right input. The decision to employ an identical number of samples as the number of scales for the CWT emerges as a pivotal factor. This approach underpins the high accuracy and renders supplementary algorithms superfluous. Furthermore, this research underscores the versatility of this approach, showcasing its effectiveness across diverse networks and scenarios. Wavelet transform, after rigorous experimentation, emerges as a reliable tool for capturing transient fault characteristics with an optimal balance between time and frequency resolutions. [ABSTRACT FROM AUTHOR]

Recent advancements in the Internet of Things (IoT) wearable devices such as wearable inertial sensors have increased the demand for precise human activity recognition (HAR) with minimal computational resources. The wavelet transform, which offers excellent time-frequency localization characteristics, is well suited for HAR recognition systems. Selecting a mother wavelet function in wavelet analysis is critical, as optimal selection improves the recognition performance. The activity time signals data have different periodic patterns that can discriminate activities from each other. Therefore, selecting a mother wavelet function that closely resembles the shape of the recognized activity's sensor (inertial) signals significantly impacts recognition performance. This study uses an optimal mother wavelet selection method that combines wavelet packet transform with the energy-to-Shannon-entropy ratio and two classification algorithms: decision tree (DT) and support vector machines (SVM). We examined six different mother wavelet families with different numbers of vanishing points. Our experiments were performed on eight publicly available ADL datasets: MHEALTH, WISDM Activity Prediction, HARTH, HARsense, DaLiAc, PAMAP2, REALDISP, and HAR70+. The analysis demonstrated in this paper can be used as a guideline for optimal mother wavelet selection for human activity recognition. [ABSTRACT FROM AUTHOR]

One of the most significant features of solar activity is its variability in a wide range of periods with the dominance of the 11-year cycle or the Schwabe cycle. In this article, a wavelet analysis of solar activity data for 1000–1700 obtained using the number of auroras taking into account the contribution of the geomagnetic field taken into account is performed. The results show the stable presence of an 11-year cycle throughout 1000–1700. It is found that there is a systematic increase in the Schwabe cycle length in 1000–1350, after which its decrease is observed. At the same time, the length of the solar cycle increases during the Oort (13 years), Wolf (14 years), and Spörer (14–15 years) grand minima. It is established that the correlation between the amplitude and length of the solar cycle was preserved over the entire time interval of 1000–1700, but its sign changed. It is also found that the correlation between the amplitude of the cycle and the length of the previous cycle is stronger than the correlation between the amplitude and the length of the same cycle. This result is similar to what is known previously for instrumental series. However, we show that this pattern persists over a much longer time interval, and it does not depend on the sign of the correlation. The article also provides indications of the existence of solar activity variations in 1000–1550 with a period of 30–40 years. [ABSTRACT FROM AUTHOR]

The paper presents some case spectral analysis of zero-sequence voltages and currents in an example industrial power distribution network. The network layout is based on typical power delivery networks in underground coal mines. Ground fault simulations have been made using an ATP/EMTP program. Due to the high environmental risks, the reliability of the protection relay operation related to their selectivity plays an important role. This paper tries to find the reasons for nonselective operation and unnecessary tripping in extensive mine cable networks, particularly with large power sources of higher-order harmonics. It was found that in transient states—due to the decaying oscillations occurring in complex RLC circuits—the results of short time measurements of the criterion values for ground fault protective relays can be overestimated (particularly for small values of ground resistance) and lead to nonselective tripping of a healthy cable line. Therefore, it might be advisable to increase the integration time used for measuring rms values. Also, if there is a significant level of higher harmonics in the industrial network generated by high-power converters, it should be noted that the higher harmonics of the ground fault current and currents measured by ground fault protection relays assume much higher values, which may also cause nonselective tripping. In this case, it may be advisable to use higher harmonic filters in the measuring circuits and to select a sufficiently high sampling frequency in the digital protective relays. [ABSTRACT FROM AUTHOR]

This work investigates the significance of Hilbert domain characterization of wavelet packets in classifying different emotion of speech signal. The goal of this paper is to create a new emotional speech database and introduce a new feature extraction approach that can recognize various emotions. The proposed feature, wavelet cepstral coefficients (WCC) are based on Hilbert spectrum analysis of the wavelet packet of the speech signal. The speaker-independent machine learning models are developed using multiclass support vector machine (SVM) and k-nearest neighbourhood (KNN) classifier. The approach is tested with newly developed Telugu Indian database and the EMOVO (Italian emotional speech) database. Our proposed wavelet features achieve a peak accuracy of 73.5%, further boosted by NCA feature selection by 3–5%, resulting in an improved unweighted average recall (UAR) of 78% for database 1 and 87.50% for database 2, employing optimal wavelet features in conjunction with SVM classification. The proposed features outperformed the baseline Mel-frequency cepstral coefficients (MFCC) feature. The performance of newly formulated features is better than other existing methodologies tested with different language databases. [ABSTRACT FROM AUTHOR]

To solve fractional partial differential equations (FPDEs) under various physical conditions, this study developed a novel method known as the Hermite wavelet method employing the functional integration matrix. The method that has been suggested is based on the Hermite wavelet collocation process. To determine the solution of the fractional differential equations, the Caputo fractional derivative operator of order α ∈ (0 , 1 ] is used. With the use of appropriate grid points, this method converts FPDEs into a system of nonlinear algebraic equations. We achieve a solution by solving these nonlinear algebraic equations by the Newton–Raphson method. Tables and graphs show that the suggested method produces superior results. We provide various illustrative examples to establish the effectiveness of the suggested concept, and the outcomes support the applicability of the suggested strategy. Obtained results are numerically expressed in terms of absolute errors. Finally, convergence analyses are discussed as some theorem with proof. [ABSTRACT FROM AUTHOR]

Deep learning (DL) techniques have recently attracted considerable attention in the field of seismic data denoising. However, most DL-based seismic denoising models require a considerable number of paired noisy–clean samples for training, which limits their application in practice. In this paper, a novel self-supervised DL scheme for noise attenuation in reflection seismic data is proposed based on the Neighbor2Neighbor strategy. The proposed method adopts a U-shaped convolutional network as the main network and incorporates regularization loss during training to improve the stability of network training, thereby constructing an end-to-end self-learning process for seismic data denoising. Specifically, a neighborhood subsampling workflow is built on single noisy seismic data to generate paired noisy images for training. Next, an unsupervised evaluation metric, grounded solely in noisy data, is adopted to quantitatively evaluate the performance of the denoising algorithm without labeled data. The application to synthetic datasets suggests that the proposed method achieves better denoising results than traditional (e.g., f−x deconvolution and wavelet analysis) and self-supervised methods, and rivals a widely used supervised DL model, the denoising convolution neural network. In real application to seismic data denoising without noise-free labels, the proposed method more effectively suppresses migration artifacts and random noise while better preserving the signal integrity compared to other methods. These improvements demonstrate that the proposed method is an independent, novel, and powerful data-driven DL scheme suitable for real seismic data denoising. [ABSTRACT FROM AUTHOR]

Pulsar time has different physical mechanisms from atomic time, and these two time-scales are complementary in terms of stability performance. The joint timekeeping of pulsar and atomic time is currently an important application direction. Our research utilizes a wavelet analysis method to perform multiscale analysis of time signals in both the time and frequency domains, and assigns corresponding weights to different frequency bands for synthesis, which solves the problem of balancing long- and short-term stability in unified weighted synthesis. In this paper, a joint time-scale APT (atomic pulsar time) is obtained by a fusion of pulsar ensemble time (EPT) and atomic time TA(NTSC) (AT) based on wavelet analysis. The results show that APT effectively integrates the stability advantages of both EPT and AT, and the short-term stability of APT at 0.4 yr is consistent with that of AT, reaching 5.6 × 10−15 as evaluated by σ z variance. The stability of APT remains consistent with EPT after 3.2 yr, and the long-term stability at 13.1 yr is close to 1.3 × 10−16, perfectly retaining the excellent long-term stability characteristics of pulsar time. The root mean square of APT's clock difference relative to TT(BIPM) is much lower than that of TA(NTSC). Moreover, we propose ways to improve the stability of APT through experimental comparisons. The results indicate that APT has the potential to serve as a reference for steering UTC(NTSC) in the future, improving the long-term timekeeping capability of local atomic time in China, thereby comprehensively improving the stability and reliability of China's time reference. [ABSTRACT FROM AUTHOR]

The development of smart homes, equipped with devices connected to the Internet of Things (IoT), has opened up new possibilities to monitor and control energy consumption. In this context, non-intrusive load monitoring (NILM) techniques have emerged as a promising solution for the disaggregation of total energy consumption into the consumption of individual appliances. The classification of electrical appliances in a smart home remains a challenging task for machine learning algorithms. In the present study, we propose comparing and evaluating the performance of two different algorithms, namely Multi-Label K-Nearest Neighbors (MLkNN) and Convolutional Neural Networks (CNN), for NILM in two different scenarios: without and with data augmentation (DAUG). Our results show how the classification results can be better interpreted by generating a scalogram image from the power consumption signal data and processing it with CNNs. The results indicate that the CNN model with the proposed data augmentation performed significantly higher, obtaining a mean F1-score of 0.484 (an improvement of + 0.234), better than the other methods. Additionally, after performing the Friedman statistical test, it indicates that it is significantly different from the other methods compared. Our proposed system can potentially reduce energy waste and promote more sustainable energy use in homes and buildings by providing personalized feedback and energy savings tips. [ABSTRACT FROM AUTHOR]

Birds from at least a dozen orders engage in dustbathing, including Galliformes. Dustbathing is generally considered a behavioural need for poultry. It involves a precise and orderly sequence of movements repeated over time. The most characteristic movement involves tossing the dust with the wings and undulating the body beneath the dust shower. Thus, repetitive changes in body position during dustbathing could be automatically detected through data processing of body‐mounted accelerometer recordings. The approach was tested in 13 adult male Japanese quail (Coturnix japonica) fitted with a body mounted triaxial accelerometer. Behaviour was video‐recorded for at least 6 h. Observations showed that when the animal lies on its left‐ or right‐side during dustbathing, the lateral (swaying) component of the acceleration vector adopts values of +1 or −1, respectively. Analysis shows that the bird repeats these shifts in body position every 25–60 s. The wavelet analysis (i.e. complex Morlet continuous wavelet transform (CWT)) detected this oscillatory behaviour within the time series as higher power values. This characteristic was used to automate the detection of dustbathing events, for which a threshold value for the maximum power value estimated was established for the corresponding range of scales between 25 and 60 s. The overall general accuracy of this classification method for dustbathing detection was 80%, with individual variations falling within the range of 66%–100%. Finally, an example of the potential of this method in the study of temporal dynamics, such as daily rhythms of dustbathing, is provided. Our results show that combining accelerometry and wavelet analysis could be useful for the assessment of intra‐ and inter‐individual variability in dustbathing dynamics over long‐term studies, even within large complex environments, such as natural habitats or breeding facilities. Moreover, this approach could open doors for future in‐depth studies exploring the relationship between dustbathing and poultry welfare. [ABSTRACT FROM AUTHOR]

The Next Generation Simulation (NGSIM) vehicle trajectory dataset is a significant public dataset in the field of transportation. However, the presence of outliers in the dataset due to measurement errors, environmental influences, and data transmission errors undermines the reliability of research based on this dataset. To address this issue, this study proposes a vehicle trajectory reconstruction method for NGSIM based on the isolation forest algorithm, Gaussian kernel regression algorithm, and wavelet analysis. Firstly, the isolation forest algorithm is employed to detect outliers. Secondly, the Gaussian kernel regression algorithm is utilized to correct these outliers. Lastly, wavelet analysis is applied for denoising purposes. Through experimental analysis of the NGSIM vehicle trajectory dataset 1-80, the results demonstrate that using Sym3 as the basis function yields optimal denoising effects whether by correcting original velocity data with Gaussian kernel algorithms followed by wavelet denoising or directly applying wavelet denoising alone. The signal to noise ratio (SNR) and root mean square error (RMSE) values obtained from correcting original data with Gaussian kernel algorithms followed by wavelet denoising are 24.02 and 1.40 respectively. These values indicate higher SNR and lower RMSE compared to directly applying wavelet denoising on original data alone. Wavelet analysis exhibits superior denoising effects when applied after correcting original data with Gaussian kernel algorithms while preserving relevant characteristics of the original data. [ABSTRACT FROM AUTHOR]

The cost function gradient vanishing or exploding problem and slow convergence speed are key issues when training deep neural networks (DNNs). In this paper, we investigate the forward and backward propagation processes of DNN training and explore the properties of the activation function and derivative function (ADF) employed. The outputs' distribution of ADF with near-zero mean is proposed to reduce gradient problems. Additionally, the constant energy transfer of propagating data in the training process is also proposed to speed up convergence further. Based on wavelet frame theory, we derive a novel ADF, i.e., tight frame wavelet activation function (TFWAF) and tight frame wavelet derivative function (TFWDF) of the Mexican hat wavelet, to stabilize and accelerate DNN training. The nonlinearity of wavelet functions can strengthen the learning capacity of DNN models, while the sparse property of wavelets derived can reduce the overfitting problem and enhance the robustness of models. Experiments demonstrate that the proposed method stabilizes the DNN training process and accelerates convergence. [ABSTRACT FROM AUTHOR]

Gaining a comprehensive understanding of the characteristics and propagation of precipitation-based meteorological drought to terrestrial water storage (TWS)-derived hydrological drought is of the utmost importance. This study aims to disentangle the frequency–time relationship between precipitation-derived meteorological and TWS-based hydrological drought from June 2002 to June 2017 based on the Standardized Precipitation Index (SPI) and Standardized Terrestrial Water Storage Index (STI) by employing wavelet coherence rather than a traditional correlation coefficient. The possible influencing factors on drought propagation in 28 regions across the world are examined. The results show that the number of drought months detected by the STI is higher than that detected by the SPI worldwide, especially for slight and moderate drought. Generally, TWS-derived hydrological drought is triggered by and occurs later than precipitation-based meteorological drought. The propagation characteristics between meteorological and hydrological droughts vary by region across the globe. Apparent intra-annual and interannual scales are detected by wavelet analysis in most regions, but not in the polar climate region. Drought propagation differs in phase lags in different regions. The phase lag between hydrological and meteorological drought ranges from 0.5 to 4 months on the intra-annual scale and from 1 to 16 months on the interannual scale. Drought propagation is influenced by multiple factors, among which the El Niño–Southern Oscillation, North Atlantic Oscillation, and potential evapotranspiration are the most influential when considering one, two, or three factors, respectively. The findings of this study improve scientific understanding of drought propagation mechanisms over a global scale and provide support for water management in different subregions. [ABSTRACT FROM AUTHOR]

Recently, several groups have demonstrated two-qubit gate fidelities in semiconductor spin qubit systems above 99%. Achieving this regime of fault-tolerant compatible high fidelities is nontrivial and requires exquisite stability and precise control over the different qubit parameters over an extended period of time. This motivates the search for the efficient calibration of qubit control parameters against different sources of micro- and macroscopic noise and methods for noise analysis. Here, we present several single- and two-qubit parameter feedback protocols, optimized for and implemented in the state-of-the-art fast field-programmable gate array hardware. Furthermore, we present a wavelet-based analysis on feedback data collected over a ∼9 h time frame to gain insight into the different sources of noise in the system. Scalable feedback is an outstanding challenge and the presented implementation and analysis gives insight into the benefits and drawbacks of qubit parameter feedback, as feedback related overhead increases. This work demonstrates a pathway toward robust qubit parameter feedback and systematic noise analysis, crucial for mitigation strategies toward systematic high-fidelity qubit operation compatible with quantum error correction protocols. [ABSTRACT FROM AUTHOR]

Animal behavior emerges from integration of many processes with different spatial and temporal scales. Dynamical behavioral patterns, including daily and ultradian rhythms and the dynamical microstructure of behavior (i.e., autocorrelations properties), can be differentially affected by external cues. Identifying these patterns is important for understanding how organisms adapt to their environment, yet unbiased methods to quantify dynamical changes over multiple temporal scales are lacking. Herein, we combine a wavelet approach with Detrended Fluctuation Analysis to identify behavioral patterns and evaluate changes over 42-days in mice subjected to different dietary restriction paradigms. We show that feeding restriction alters dynamical patterns: not only are daily rhythms modulated but also the presence, phase and/or strength of ~12h-rhythms, as well as the nature of autocorrelation properties of feed-intake and wheel running behaviors. These results highlight the underlying complexity of behavioral architecture and offer insights into the multi-scale impact of feeding habits on physiology. Wavelet analysis of mice wheel running and feeding time series unveils the impact of food restriction on dynamical patterns beyond circadian rhythms, highlighting the complex nature of mouse behavior across a spectrum of temporal scales. [ABSTRACT FROM AUTHOR]

Based on monthly economic data spanning from January 2015 to December 2022, we have established an analytical framework to examine the "Russia-Ukraine conflict—financial market pressure and energy market—China carbon emission trading prices." To achieve this objective, we developed indices for financial system pressure, the energy market, and investor sentiment, applying a mediation effects model to validate their transmission mechanisms. Subsequently, the TVP-SV-VAR model was employed to scrutinize the nonlinear impact of the Russia-Ukraine conflict on the valuation of China's carbon emission trading rights. This model integrates time-varying parameters (TVP) and stochastic volatility (SV), utilizing Markov Chain Monte Carlo (MCMC) technology for parameter estimation. Finally, various wavelet analysis techniques, including continuous wavelet transform, cross-wavelet transform, and wavelet coherence spectrum, were applied to decompose time series data into distinct time-frequency scales, facilitating an analysis of the lead-lag relationships within each time series. The research outcomes provide crucial insights for safeguarding the interests of trading organizations, refining the structure of the carbon market, and mitigating systemic risks on a global scale. [ABSTRACT FROM AUTHOR]

Airborne gamma-ray spectrometry (AGS) is a technical method that uses an aircraft as the carrier to measure airborne geophysical fields. Due to the high detection efficiency and low working cost, AGS is suitable for large-scale operations and measurements conducted jointly across land and sea. However, the accuracy is strongly associated with the detection arrangement, which may easily cause banding anomalies distributed along lines. The line unit levelling method is used to eliminate such differences occurring between the banding anomaly and mineralisation anomaly; however, artificial influence is inevitable as the parameter setting depends on experience. This paper proposes a new line unit levelling method combined with the wavelet transform theory and variation coefficient theory. This method adopts wavelet transform to perform multi-scale decomposition on the data per measurement line unit, and effectively removes nongeological background information through soft threshold filtering. The results prove that this method can significantly reduce the errors caused by time domain batches, narrow the anomaly range, and improve the recognition accuracy of ore-causing anomaly points. Moreover, the shape of the anomalies is consistent with the ground anomaly inspection results, and the distribution of radionuclides can be restored in the surveying area. [ABSTRACT FROM AUTHOR]

Gas turbines play a vital role in gas transportation and power generation, but they are prone to instability phenomena that can lead to vibrations, shorten equipment lifespan, and result in catastrophic failures. To tackle these challenges, a paper introduces an integrated approach that leverages advanced techniques like Fourier transform, Neuro-Fuzzy systems, and wavelet analysis for continuous monitoring of the MS5002C turbine's condition. The proposed method begins by collecting operational data and utilizing the Fourier transform to measure vibratory quantities, accurately representing their evolution through spectral data obtained from the analyzed signals. Adaptive inference-based algorithms of neuro-fuzzy systems are then employed to generate turbine failure indicators. This approach enables the development of a model-based fault detection method that compares the actual turbine operation with the estimated operation derived from a pre-established model, enabling the classification of detected faults. To enhance decision-making quality, evaluation, and validation of the diagnostic strategy's performance, a multi-resolution analysis based on the wavelet transform is applied. The presented results from various implementation and validation tests demonstrate the effectiveness of this intelligent diagnostic approach in detecting and analyzing gas turbine vibrations. The paper exhibits promising outcomes in real-time monitoring, ensuring the operational safety of the turbine. [ABSTRACT FROM AUTHOR]

Wavelets analysis has become a powerful mathematical tool to decompose a series and to provide its frequent and temporal features. Despite the fact that many researchers have begun testing this approach on financial and economic data to detect and treat outliers, it still not widely applied for longitudinal data. Therefore, this article proposes two algorithms for improving the accuracy of outlier detection in longitudinal data using Wavelets Decomposition, namely, Wavelets Decomposition for Outliers Detection and Handling across Subjects (WDODHAS), and Wavelets Decomposition for Outliers Detection and Handling within Subjects (WDODHWIS). The results show that Wavelets Decomposition is capable of detecting and handling outliers without erasing them, as well as highlighting hypothetical scenarios when these observations cannot be handled. [ABSTRACT FROM AUTHOR]

The prominent features of the primary instabilities of a turbulent source flow between finite-span parallel rings with spatially modulated inflow conditions rotating at angular rate Ω are investigated. Two different two-dimensional instabilities that occur at large ( α Q ≳ 1. 00) and low ( α Q ≲ 0. 65) flow rates have been traced back to two different physical mechanisms related to jet-wake and mean-core flow, respectively. The effect of the radial aspect ratios Γ on the instability characteristics of the flow between parallel rings is also considered in this paper. Numerical URANS simulations are conducted for three different radial aspect ratios, i.e. Γ = 1. 25 , 1.5, and 2, the corresponding cross-sectional aspect ratios Λ = 2 (Γ − 1) × 3. 325 , and two conceptually different inflow conditions. Their impact on the rotating instabilities in terms of instability mode number and instability frequency is the main focus of this study and is analyzed by fast Fourier transform (FFT) and wavelet analysis. Realistic inflow conditions with a leakage flow are found to overtake the instability for small radius ratios and affect the primary instabilities for large radius ratios. [ABSTRACT FROM AUTHOR]

In this paper, we present a comprehensive study of wavelet theory with a focus on structural damage detection. A new example of the application of operational modal analysis (OMA) techniques to a concrete railway arch bridge located over the Kalix river in Lingforsen, Sweden is presented. Results from the OMA techniques are used for finite element model (FEM) updating of this concrete railway arch bridge. Further, a new case study for sensor placement on Her0ysund bridge located in Nordland, Norway to conduct OMA is discussed in detail. Moreover, artificial intelligence algorithms that can be useful for addressing the problem of missing data sets in structural health monitoring technologies are reviewed. [ABSTRACT FROM AUTHOR]

The ENSO phenomenon is associated with below average rainfall and influences the climate regime of southern Africa. With the advent of climate change, drought frequencies and magnitudes have worsened in the developing world and this in turn negatively impacts the natural environment and communities' livelihoods. This study evaluated the relationship between El Niño-induced drought and reservoir water levels over the Albasini Dam Catchment (ADC) areas in Limpopo Province, South Africa. Standardised indices (i.e., SPI and SSI) were used to define drought events over the study area. Mann–Kendall and Sequential Mann–Kendall were used for trends analysis as well as correlation and wavelet coherence to evaluate the relationship between variables of interest. There exists a relationship between El Niño-induced drought event and reservoir water levels. This was shown by the correlation between drought indices and reservoir water levels with the coefficient of determination being stronger at the 12th timescale (i.e., 0.743 and 0.59) compared to the 6th timescale (i.e., 0.07 and 0.44) for both precipitation and streamflow indices, respectively. Wavelet analysis further showed that there existed a phased relationship between the two variables. Although there are other factors that may affect reservoir water resources, these study findings show that El Niño-induced drought also negatively affect water resources. Therefore, this study recommends the development of multidimensional and multiscale management strategies to minimise drought impacts and adaptation in the region. [ABSTRACT FROM AUTHOR]

Morphological features of sagittal otoliths in Pagellus acarne, P. bogaraveo, and P. erythrinus samples collected from the Aegean Sea were used to assess the variability both between Pagellus species and between otolith pairs employing scanning electron microscopy (SEM) and contour shape analysis. Twenty-one otolith morphological characters were compared in detail using SEM analysis. Twelve of these characters differ between Pagellus species and are suitable for distinguishing between the three congeneric Pagellus species. Wavelet functions of otolith contour shape were compared for six Pagellus groups. The wavelet functions obtained from the contour shape analysis showed high variability in the antero-dorsal, postero-dorsal, and antero-ventral regions of otoliths among the six groups analyzed. The results of principal component analysis (PCA) of the wavelet data provided a clear distinction between the groups under study. Cluster analysis supported the PCA results and basically divided them into two main branches. One of these branches contained only P. erythrinus, while P. acarne and P. bogaraveo were present in the other branch. This is the first study to provide detailed morphological characters and wavelet analysis of left- and right-side otoliths in these three congeneric Pagellus species from the Aegean Sea. [ABSTRACT FROM AUTHOR]

In this study, we performed periodicity analyses of selected daily solar (flare index, coronal index, number of coronal mass ejections), geomagnetic (planetary equivalent range index, disturbance storm time index, interplanetary magnetic field) and cosmic ray indices for the last Solar Cycle 24 (from December 2008 to December 2019). To study the periodic variation of the above-listed datasets, the following analysis methods were applied; multi-taper method, Morlet wavelet, cross-wavelet transform and wavelet coherence analysis. The outcome of our analyses revealed the following. (i) The 25–33 days solar rotation periodicities exist in all datasets without any exception in the MTM power spectra. (ii) Except for the solar rotation periodicity, all periods show data preference, and they appear around the investigated cycle's maximum phase. (iii) When comparing the phase relations between periodicities in the used datasets, they exhibit a gradual transition from small to large periods. For short-term periodicities, there are no phase relations but a mixed phase, whereas for high periodicities, there are complete phase/antiphase transitions. (iv) All identified flare index periodicities are common to all other datasets examined in this investigation. [ABSTRACT FROM AUTHOR]

The indirect approaches based on the responses of specially designed moving vehicles on bridges to extract bridge dynamic properties have received great research attention. Wavelet transform (WT) is shown to be a useful tool for the analysis of vehicle responses and bridge damage detection in the vehicle-bridge interaction system. However, past studies have demonstrated that road surface roughness poses difficulty in applying wavelet analysis to successfully detect bridge damage. In this paper, a two-step method is proposed for bridge damage detection based on WT analysis of the residual contact response of the front and rear vehicle wheels of a four degrees of freedom (4-DOF) moving vehicle to reduce the influence of road surface roughness. In the first step, the generalized Kalman filter under unknown input (GKF-UI) developed by the authors is used to identify the unknown contact forces and the state vector of the vehicle. Due to the advantages of GKF-UI, acceleration and displacement sensors can be installed conveniently on the vehicle body instead of on the wheel axles in previous studies. Then, the contact-point bridge deflection responses of the front and rear vehicle wheels are estimated from the identified contact force and vehicle state. In the second step, the residual contact-point bridge deflection response is obtained by subtracting the front contact-point bridge deflection and rear contact-point bridge deflection with a time shift to reduce the influence of road surface roughness. Then, the locations of bridge damage can be identified based on WT of the singularities of residual contact-point bridge deflection response. Numerical examples of bridge damage detection demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]