Increasing Visual Biofeedback Scale Changes Postural Control Complexity

Visual biofeedback (vFB) during quiet stance has been shown to improve postural control. While this improvement has been quantified by a reduction in the center of pressure (COP) sway, the effect on COP complexity remains unexplored. As such, 20 young adults (12 females; aged 23.63 ± 3.17 years) were asked to remain in a static upright posture under different visual biofeedback magnitude (no feedback [NoFB], magnified by 1 [vFB1], magnified by 5 [vBF5] and magnified by 10 [vBF10]). In addition to confirming, through traditional COP variables (i.e. standard deviation, mean velocity, sway area), that vFB scaling improved postural control, results also suggested changes in COP complexity. Specifically, sample entropy and wavelet analysis showed that increasing the vFB scale from 1:1 to 1:5 and 1:10 led to a more irregular COP and a shift toward higher frequency. Together, and particularly from a complexity standpoint, these findings provided additional understandings of how vFB and vFB scaling improved postural control.

Using wavelet transform and hybrid CNN - LSTM models on VOC & ultrasound IoT sensor data for non-visual maize disease detection

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.

Effects of caffeine supplementation on anaerobic power and muscle activity in youth athletes

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.

A method for evaluating of RNA's coding potential using the interaction effects of open reading frames and high-energy scalograms

The identification and function determination of long non-coding RNAs (lncRNAs) can help to better understand the transcriptional regulation in both normal development and disease pathology, thereby demanding methods to distinguish them from protein-coding (pcRNAs) after obtaining sequencing data. Many algorithms based on the statistical, structural, physical, and chemical properties of the sequences have been developed for evaluating the coding potential of RNA to distinguish them. In order to design common features that do not rely on hyperparameter tuning and optimization and are evaluated accurately, we designed a series of features from the effects of open reading frames (ORFs) on their mutual interactions and with the electrical intensity of sequence sites to further improve the screening accuracy. Finally, the single model constructed from our designed features meets the strong classifier criteria, where the accuracy is between 82% and 89%, and the prediction accuracy of the model constructed after combining the auxiliary features equal to or exceed some best classification tools. Moreover, our method does not require special hyper-parameter tuning operations and is species insensitive compared to other methods, which means this method can be easily applied to a wide range of species. Also, we find some correlations between the features, which provides some reference for follow-up studies.

Simulating daily PM2.5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data

Accurate PM2.5 concentrations predicting is critical for public health and wellness as well as pollution control. However, traditional methods are difficult to accurately predict PM2.5. An adaptive model coupled with artificial neural network (ANN) and wavelet analysis (WANN) is utilized to predict daily PM2.5 concentrations with remote sensing and surface observation data. The four evaluation metrics, namely Pearson correlation coefficient (R), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE), are utilized to evaluate the performances of the artificial neural network (ANN) and WANN methods. From the predicting results, The WANN model has a higher R (R = 0.9990) during the testing period compared with R (R = 0.6844) based on the ANN model. Similarly, the WANN model has a lower MAPE (3.6988%), RMSE (1.0145 μg/m3), MAE (1.3864 μg/m3), compared with MAPE (80.0086%), RMSE (16.5838 μg/m3), MAE (12.2420 μg/m3) of the ANN. In addition, comparing the outcomes of the proposed WANN method with the ANN method, it was observed that the error during the training and verification period has decreased significantly. Furthermore, the statistical methods are used to analyze WANN and ANN, showing that WANN has higher training accuracy and better stability. Therefore, it is feasible to establish WANN to predict PM2.5 concentrations (1 day in advance) by using remote sensing and surface observation data.

Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India

The intensity and frequency of diverse hydro-meteorological disasters viz., extreme droughts, severe floods, and cyclones have increasing trends due to unsustainable management of land and water resources, coupled with increasing industrialization, urbanization and climate change. This study focuses on the forecasting of drought using selected Artificial Neural Network (ANN)-based models to enable decision-makers to improve regional water management plans and disaster mitigation/reduction plans. Four ANN models were developed in this study, viz., one conventional ANN model and three hybrid ANN models: (a) Wavelet based-ANN (WANN), (b) Bootstrap based-ANN (BANN), and (c) Wavelet-Bootstrap based-ANN (WBANN). The Standardized Precipitation Evapotranspiration Index (SPEI), the best drought index identified for the study area, was used as a variable for drought forecasting. Three drought indices, such as SPEI-3, SPEI-6 and SPEI-12 respectively representing "short-term", "intermediate-term", and "long-term" drought conditions, were forecasted for 1-month to 3-month lead times for six weather stations over the study area. Both statistical and graphical indicators were considered to assess the performance of the developed models. For the hybrid wavelet model, the performance was evaluated for different vanishing moments of Daubechies wavelets and decomposition levels. The best-performing bootstrap-based model was further used for analysing the uncertainty associated with different drought forecasts. Among the models developed for drought forecasting for 1 to 3 months, the performances of the WANN and WBANN models are superior to the simple ANN and BANN models for the SPEI-3, SPEI-6, and SPEI-12 up to the 3-month lead time. The performance of the WANN and WBANN models is the best for SPEI-12 (MAE = 0.091-0.347, NSE = 0.873-0.982) followed by SPEI-6 (MAE = 0.258-0.593; NSE = 0.487-0.848) and SPEI-3 (MAE = 0.332-0.787, NSE = 0.196-0.825) for all the stations up to 3-month lead time. This finding is supported by the WBANN analyze uncertainties as narrower band width for SPEI-12 (0.240-0.898) as compared to SPEI-6 (0.402-1.62) and SPEI-3 (0.474-2.304). Therefore, the WBANN model is recommended for the early warning of drought events as it facilitates the uncertainty analysis of drought forecasting results.

Diversification evidence of bitcoin and gold from wavelet analysis

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.

Impact of climate change on climate extreme indices in Kaduna River basin, Nigeria

This study examined the impact of climate change on climate extreme indices in the Kaduna River basin, Nigeria. Large-scale atmospheric variables derived from the Global Climate Model (GCM), Coupled Model Intercomparison Project Phase 5 (CMIP5) (CanESM2) were used to develop a high-resolution climate using a Statistical Down Scaling Model. The adapted Caussinus-Mestre algorithm for homogenizing networks of temperature series and multivariate bias correction based on an N-dimension probability function were used to homogenize and correct the climate data, respectively. Fifteen climate extreme indices were computed using RClimdex. The coefficient of variance, Kruskal-Wallis test, and the modified Mann-Kendall test were used to assess the variation and trends. Wavelet analysis was used to determine the periodicities of the indices (1980-2020). The findings revealed a significant warming trend with low variability of temperature indices. The moderate variability with an insignificant decreasing trend was found for rainfall indices. Similarly, the future climate indices indicate a continuing positive trend in the temperature extreme indices. The majority of climate indices have a periodicity of less than or equal to 10 years for high frequency, except for PRCPTOT, R10MM, R20MM, Rx5day, SDII, TN90p, and TX90p for temperature indices. The findings conclude that the periodicity pattern of climate extreme indices is related to atmospheric phenomena (such as quasi-biennial oscillation, QBO), which indicate the impact of climate change. As a result, this can serve as an early warning for possible extreme event occurrences in the basin. The CMIP6 should be used to compare with the results of this study to provide a detailed assessment of the current implication of climate change on the catchment.

Enhanced rainfall prediction performance via hybrid empirical-singular-wavelet-fuzzy approaches

Rainfall is a vital process in the hydrological cycle of the globe. Accessing reliable and accurate rainfall data is crucial for water resources operation, flood control, drought warning, irrigation, and drainage. In the present study, the main objective is to develop a predictive model to enhance daily rainfall prediction accuracy with an extended time horizon. In the literature, various methods for the prediction of daily rainfall data for short lead times are presented. However, due to the complex and random nature of rainfall, in general, they yield inaccurate prediction results. Generically, rainfall predictive models require many physical meteorological variables and consist of challenging mathematical processes that require high computational power. Furthermore, due to the nonlinear and chaotic nature of rainfall, observed raw data typically has to be decomposed into its trend cycle, seasonality, and stochastic components before being fed into the predictive model. The present study proposes a novel singular spectrum analysis (SSA)-based approach for decomposing observed raw data into its hierarchically energetic pertinent features. To this end, in addition to the stand-alone fuzzy logic model, preprocessing methods SSA, empirical mode decomposition (EMD), and commonly used discrete wavelet transform (DWT) are incorporated into the fuzzy models which are named as hybrid SSA-fuzzy, EMD-fuzzy, W-fuzzy models, respectively. In this study, fuzzy, hybrid SSA-fuzzy, EMD-fuzzy, and W-fuzzy models are developed to enhance the daily rainfall prediction accuracy and improve the prediction time span up to 3 days via three (3) stations' data in Turkey. The proposed SSA-fuzzy model is compared with fuzzy, hybrid EMD-fuzzy, and widely used hybrid W-fuzzy models in predicting daily rainfall in three distinctive locations up to a 3-day time horizon. Improved accuracy in predicting daily rainfall is provided by the SSA-fuzzy, W-fuzzy, and EMD-fuzzy models compared to the stand-alone fuzzy model based on mean square error (MSE) and the Nash-Sutcliffe coefficient of efficiency (CE) model assessment metrics. Specifically, the advocated SSA-fuzzy model is found to be superior in accuracy to hybrid EMD-fuzzy and W-fuzzy models in predicting daily rainfall for all time spans. The results reveal that, with its easy-to-use features, the advocated SSA-fuzzy modeling tool in this study is a promising principled method for its possible future implementations not only in hydrological studies but in water resources and hydraulics engineering and all scientific disciplines where future state space prediction of a vague nature and stochastic dynamical system is important.

Undersampled MRI reconstruction based on spectral graph wavelet transform

Compressed sensing magnetic resonance imaging (CS-MRI) has exhibited great potential to accelerate magnetic resonance imaging if an image can be sparsely represented. How to sparsify the image significantly affects the reconstruction quality of images. In this paper, a spectral graph wavelet transform (SGWT) is introduced to sparsely represent magnetic resonance images in iterative image reconstructions. The SGWT is achieved by extending the traditional wavelets transform to the signal defined on the vertices of the weighted graph, i.e. the spectral graph domain. This SGWT uses only the connectivity information encoded in the edge weights, and does not rely on any other attributes of the vertices. Therefore, SGWT can be defined and calculated for any domain where the underlying relations between data locations can be represented by a weighted graph. Furthermore, we present a Chebyshev polynomial approximation algorithm for fast computing this SGWT transform. The l₁ norm regularized CS-MRI reconstruction model is introduced and solved by the projected iterative soft-thresholding algorithm to verify its feasibility. Numerical experiment results demonstrate that our proposed method outperforms several state-of-the-art sparsify transforms in terms of suppressing artifacts and achieving lower reconstruction errors on the tested datasets.

A new model for classification of medical CT images using CNN: a COVID-19 case study

SARS-CoV-2 is the causative agent of COVID-19 and leaves characteristic impressions on chest Computed Tomography (CT) images in infected patients and this analysis is performed by radiologists through visual reading of lung images, and failures may occur. In this article, we propose a classification model, called Wavelet Convolutional Neural Network (WCNN) that aims to improve the differentiation of images of patients with COVID-19 from images of patients with other lung infections. The WCNN model was based on a Convolutional Neural Network (CNN) and wavelet transform. The model proposes a new input layer added to the neural network, which was called Wave layer. The hyperparameters values were defined by ablation tests. WCNN was applied to chest CT images to images from two internal and one external repositories. For all repositories, the average results of Accuracy (ACC), Sensitivity (Sen) and Specificity (Sp) were calculated. Subsequently, the average results of the repositories were consolidated, and the final values were ACC = 0.9819, Sen = 0.9783 and Sp = 0.98. The WCNN model uses a new Wave input layer, which standardizes the network input, without using data augmentation, resizing and segmentation techniques, maintaining the integrity of the tomographic image analysis. Thus, applications developed based on WCNN have the potential to assist radiologists with a second opinion in the analysis.1.

Wavelet kernel least square twin support vector regression for wind speed prediction

Wind energy is a powerful yet freely available renewable energy. It is crucial to predict the wind speed (WS) accurately to make a precise prediction of wind power at wind power generating stations. Generally, the WS data is non-stationary and wavelets have the capacity to deal with such non-stationarity in datasets. While several machine learning models have been adopted for prediction of WS, the prediction capability of primal least square support vector regression (PLSTSVR) for the same has never been tested to the best of our knowledge. Therefore, in this work, wavelet kernel-based LSTSVR models are proposed for WS prediction, namely, Morlet wavelet kernel LSTSVR and Mexican hat wavelet kernel LSTSVR. Hourly WS data is gathered from four different stations, namely, Chennai, Madurai, Salem and Tirunelveli in Tamil Nadu, India. The proposed models' performance is assessed using root mean square, mean absolute, symmetric mean absolute percentage, mean absolute scaled error and R². The proposed models' results are compared to those of twin support vector regression (TSVR), PLSTSVR and large-margin distribution machine-based regression (LDMR). The performance of the proposed models is superior to other models based on the results of the performance indicators.

The HAPPE plus Event-Related (HAPPE+ER) software: A standardized preprocessing pipeline for event-related potential analyses

Event-Related Potential (ERP) designs are a common method for interrogating neurocognitive function with electroencephalography (EEG). However, the traditional method of preprocessing ERP data is manual-editing - a subjective, time-consuming processes. A number of automated pipelines have recently been created to address the need for standardization, automation, and quantification of EEG data pre-processing; however, few are optimized for ERP analyses (especially in developmental or clinical populations). We propose and validate the HAPPE plus Event-Related (HAPPE+ER) software, a standardized and automated pre-processing pipeline optimized for ERP analyses across the lifespan. HAPPE+ER processes event-related potential data from raw files through preprocessing and generation of event-related potentials for statistical analyses. HAPPE+ER also includes post-processing reports of both data quality and pipeline quality metrics to facilitate the evaluation and reporting of data processing in a standardized manner. Finally, HAPPE+ER includes post-processing scripts to facilitate validating HAPPE+ER performance and/or comparing to performance of other preprocessing pipelines in users' own data via simulated ERPs. We describe multiple approaches with simulated and real ERP data to optimize pipeline performance and compare to other methods and pipelines. HAPPE+ER software is freely available under the terms of GNU General Public License at https://www.gnu.org/licenses/#GPL.

Convergence analysis of ammonia emissions by sector and fuel source in OECD countries from 1750 to 2019 using a new Fourier-centric wavelet approach

Although ammonia emissions are not as huge as carbon and methane emissions, they pose significant threats to ensuring environmental sustainability and productivity. However, the existing literature has paid less attention to the underlying characteristics of ammonia emissions. The chief target of this study is to investigate the stochastic convergence of ammonia emissions at the aggregate level, by sector, and by fuel source in 37 Organization for Economic Cooperation and Development countries for more than two centuries of data. Using a newly proposed Fourier-augmented wavelet unit root test, the empirical findings reveal that the relative ammonia emissions series in most Organization for Economic Cooperation and Development countries follow the unit root process in the aggregate, sectoral, and fuel-specific analyses. Therefore, these findings refer to the existence of divergence, while stochastic convergence does not exist in most cases. Having a divergent pattern of ammonia emissions has several policy implications for policymakers in the context of environmental sustainability. (i) Relative ammonia emission cannot revert to its steady-state path without policy intervention, (ii) policymakers have a chance of affecting the dynamics of ammonia emissions in Organization for Economic Cooperation and Development countries. (iii) As a policy response, the study recommends the pursuant of national environmental policies with consideration to the unique characteristics of the individual countries as the non-existence of convergence of environmental series could result in a diverse level of consciousness of environmental degradation among countries with divergent patterns on emissions levels.

Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method

BACKGROUND

The diagnosis of cardiac abnormalities based on heart sound signal is a research hotspot in recent years. The early diagnosis of cardiac abnormalities has a crucial significance for the treatment of heart diseases.

METHODS

For the sake of achieving more practical clinical applications of automatic recognition of cardiac abnormalities, here we proposed a novel fuzzy matching feature extraction method. First of all, a group of Gaussian wavelets are selected and then optimized based on a template signal. Convolutional features of test signal and the template signal are then computed. Matching degree and matching energy features between template signal and test signal in time domain and frequency domain are then extracted. To test performance of proposed feature extraction method, machine learning algorithms such as K-nearest neighbor, support vector machine, random forest and multilayer perceptron with grid search parameter optimization are constructed to recognize heart disease using the extracted features based on phonocardiogram signals.

RESULTS

As a result, we found that the best classification accuracy of random forest reaches 96.5% under tenfold cross validation using the features extracted by the proposed method. Further, Mel-Frequency Cepstral Coefficients of phonocardiogram signals combing with features extracted by our algorithm are evaluated. Accuracy, sensitivity and specificity of integrated features reaches 99.0%, 99.4% and 99.7% respectively when using support vector machine, which achieves the best performance among all reported algorithms based on the same dataset. On several common features, we used independent sample t-tests. The results revealed that there are significant differences (p < 0.05) between 5 categories.

CONCLUSION

It can be concluded that our proposed fuzzy matching feature extraction method is a practical approach to extract powerful and interpretable features from one-dimensional signals for heart sound diagnostics and other pattern recognition task.

Wavelet and Fourier augmented convergence analysis of methane emissions in more than two centuries: implications for environmental management in OECD countries

Addressing the challenges posed by pollutants is necessary to achieve Sustainable Development Goal 13, which involves climate change mitigation and enhancement of environmental quality. The convergence analysis of a pollutant provides information that can be useful to how to handle that pollutant across countries or regions, and previous studies mainly focused on carbon emission. However, the second most significant greenhouse gas, methane emission, was mostly ignored. The primary objective of this research is to investigate whether stochastic convergence of methane emissions is valid in 37 OECD (Organisation for Economic Co-operation and Development) countries using a dataset of more than two centuries. The results obtained by using a set of traditional unit root tests and a newly proposed wavelet unit root test with a Fourier function provide overwhelming evidence for these countries' divergence of methane emissions. The policy implications resulting from the empirical findings for environmental management are discussed in the relevant sections of the paper.

Low-cost nature-inspired deep learning system for PM2.5 forecast over Delhi, India

Air quality has a tremendous impact on India's health and prosperity. Air quality models are crucial tools for surveying and projecting air pollution episodes, which can be used to issue health advisories to take action ahead of time. Short-term increases in air pollution trigger many adverse health events; a fast, efficient, cost-effective, and reliable air quality prediction model would aid in minimizing the effect on health and prosperity. Deterministic models, on the other hand, are less robust in predicting the pollutant series since it is non-stationary and non-linear. Atmospheric chemistry models are computationally expensive and often rely on outdated emissions information. We propose a deep learning model in this study that integrates neural networks, fuzzy inference systems, and wavelet transforms to predict the most prominent air pollutant affecting Delhi, India i.e., PM2.5 (particulate matter of aerodynamic diameter less than or equal to 2.5 µm). We have included the main aspects of air quality models in this research i.e., less computational time (7 min approximately using I5-1035G1, 1.19 GHz processor), less resource-intensive (dependent only on the pollutant lagged values), and high spatial resolution (1 km) for forecasting air quality three days ahead. The model predictions show a significant correlation coefficient lying in [0.96,0.98], [0.86,0.93], and [0.82,0.91] with Central Pollution Control Board (CPCB) monitored data at various sites in Delhi for one, two, and three days of forecast respectively.

Linking electricity demand and economic growth in China: evidence from wavelet analysis

The study empirically examines the association between electricity demand and economic growth in China in a time-frequency framework. Wavelet coherence analysis and phase difference methods are applied to find the co-movement and causality between variables using monthly data for 1999 to 2017 time period. The results of the wavelet power spectrum show that both series have high fluctuations at high frequencies. The findings of wavelet coherence reveal co-movements between electricity demand and economic growth at different frequency levels. However, this association is stronger at low-frequency levels. Evidence from the phase difference indicates that electricity is causing economic growth with a positive sign. The results of wavelet-based correlation also show a high correlation between these two variables. For robustness analysis, linear and nonlinear causality tests are applied to find causality between variables over time. Both linear and nonlinear causality tests reveal bidirectional causality between variables. It corroborates the result of wavelet causality that both variables cause each other at different frequency levels.

Testing the safe-haven properties of gold and bitcoin in the backdrop of COVID-19: A wavelet quantile correlation approach

We test the suitability of Gold and Bitcoin as safe-haven instruments in the backdrop of the Covid-19 related equity market meltdown by implementing the newly proposed Wavelet Quantile Correlation. We employ daily returns of Bitcoin, Gold, DJIA, CAC40, NSE50, S&P 500, NASDAQ, and EUROSTOXX from 05-01-2015 to 31-12-2020. Our results show that Gold consistently exhibits safe haven properties for all the markets except NSE in the long and short run, while Bitcoin provided mixed results. We find that Gold can act as an effective hedge and diversifier as well.

How to get best predictions for road monitoring using machine learning techniques

Road condition monitoring is essential for improving traffic safety and reducing accidents. Machine learning methods have recently gained prominence in the practically important task of controlling road surface quality. Several systems have been proposed using sensors, especially accelerometers present in smartphones due to their availability and low cost. However, these methods require practitioners to specify an exact set of features from all the sensors to provide more accurate results, including the time, frequency, and wavelet-domain signal features. It is important to know the effect of these features change on machine learning model performance in handling road anomalies classification tasks. Thus, we address such a problem by conducting a sensitivity analysis of three machine learning models which are Support Vector Machine, Decision Tree, and Multi-Layer Perceptron to test the effectiveness of the model by selecting features. We built a feature vector from all three axes of the sensors that boosts classification performance. Our proposed approach achieved an overall accuracy of 94% on four types of road anomalies. To allow an objective analysis of different features, we used available accelerometer datasets. Our objective is to achieve a good classification performance of road anomalies by distinguishing between significant and relatively insignificant features. Our chosen baseline machine learning models are based on their comparative simplicity and powerful empirical performance. The extensive analysis results of our study provide practical advice for practitioners wishing to select features effectively in real-world settings for road anomalies detection.

Oil price, US stock market and the US business conditions in the era of COVID-19 pandemic outbreak

This paper contributes to Covid-19 outbreak impacts literature. We investigate the connectedness between stock market and oil prices under bullish and bearish economic conditions and uncertainty level at different investment horizons. We applied the wavelet framework on daily dataset cover the pre-COVID-19 and COVID-19 period. We find that the linkage between the economic and financial pairs is characterized by significant changes over the time during the sample period, where the huge co-movements has been identified during the pandemic period at the low scale. We show that due to lockdown policy and oil price shock, the stock return decline, the aggregate business conditions reached its lowest level and the uncertainty increase. The result indicates that the COVID-19 outbreak negatively affects the economy and the financial markets and support the sensitivity, especially between oil-stock, and economic condition and uncertainty.

Coarse-graining and the Haar wavelet transform for multiscale analysis

BACKGROUND

Multiscale entropy (MSE) has become increasingly common as a quantitative tool for analysis of physiological signals. The MSE computation involves first decomposing a signal into multiple sub-signal 'scales' using a coarse-graining algorithm.

METHODS

The coarse-graining algorithm averages adjacent values in a time series to produce a coarser scale time series. The Haar wavelet transform convolutes a time series with a scaled square wave function to produce an approximation which is equivalent to averaging points.

RESULTS

Coarse-graining is mathematically identical to the Haar wavelet transform approximations. Thus, multiscale entropy is entropy computed on sub-signals derived from approximations of the Haar wavelet transform. By describing coarse-graining algorithms properly as Haar wavelet transforms, the meaning of 'scales' as wavelet approximations becomes transparent. The computed value of entropy is different with different wavelet basis functions, suggesting further research is needed to determine optimal methods for computing multiscale entropy.

CONCLUSION

Coarse-graining is mathematically identical to Haar wavelet approximations at power-of-two scales. Referring to coarse-graining as a Haar wavelet transform motivates research into the optimal approach to signal decomposition for entropy analysis.

Computational Approaches and Tools as Applied to the Study of Rhythms and Chaos in Biology

The temporal dynamics in biological systems displays a wide range of behaviors, from periodic oscillations, as in rhythms, bursts, long-range (fractal) correlations, chaotic dynamics up to brown and white noise. Herein, we propose a comprehensive analytical strategy for identifying, representing, and analyzing biological time series, focusing on two strongly linked dynamics: periodic (oscillatory) rhythms and chaos. Understanding the underlying temporal dynamics of a system is of fundamental importance; however, it presents methodological challenges due to intrinsic characteristics, among them the presence of noise or trends, and distinct dynamics at different time scales given by molecular, dcellular, organ, and organism levels of organization. For example, in locomotion circadian and ultradian rhythms coexist with fractal dynamics at faster time scales. We propose and describe the use of a combined approach employing different analytical methodologies to synergize their strengths and mitigate their weaknesses. Specifically, we describe advantages and caveats to consider for applying probability distribution, autocorrelation analysis, phase space reconstruction, Lyapunov exponent estimation as well as different analyses such as harmonic, namely, power spectrum; continuous wavelet transforms; synchrosqueezing transform; and wavelet coherence. Computational harmonic analysis is proposed as an analytical framework for using different types of wavelet analyses. We show that when the correct wavelet analysis is applied, the complexity in the statistical properties, including temporal scales, present in time series of signals, can be unveiled and modeled. Our chapter showcase two specific examples where an in-depth analysis of rhythms and chaos is performed: (1) locomotor and food intake rhythms over a 42-day period of mice subjected to different feeding regimes; and (2) chaotic calcium dynamics in a computational model of mitochondrial function.

COVID-19 and stock exchange return variation: empirical evidences from econometric estimation

This research looked at the effects of COVID-19 on a number of the world's most important stock exchanges, as well as the empirical relation between the COVID-19 wave and stock market volatility. In order to plan proper portfolio diversification in international financial markets, researchers must examine COVID-19 anxiety in relation to stock market volatility. The stock market volatility connected with the COVID-19 pandemic was measured using AR(1)-GARCH(1,1). COVID-19 fear, according to our research, is the ultimate driver of public attention and stock market volatility. The findings show that throughout the pandemic, stock market performance and GDP growth both declined significantly due to average increases. Furthermore, a 1% increase in COVID-19 causes a 0.8% and 0.56% decline in stock return and GDP, respectively. The stock market, on the other hand, showed a slight movement in GDP growth. Furthermore, the COVID-19 pandemic reported cases index, death index, and global panic index all influenced public perceptions of purchasing and selling. As a result, rather than investing in stocks, it is recommended that you invest in gold. The research also makes policy recommendations for important stakeholders. We look to examine how stock returns respond dynamically to unanticipated changes in the COVID-19 scenarios, as well as the uncertainty that comes with a pandemic. Using daily data from Canada and the USA, we conclude that a spike in COVID-19 instances has a negative impact on the stock market in general. Furthermore, in both the increase and decline scenarios in Canada, the stock return reactions are asymmetric. The disparity is due to the unfavorable impact of the pandemic's unpredictability. We also discovered that uncertainty had a negative impact on the US stock market. The magnitude, however, is insignificant.

An evaluation of the causal effect between air pollution and renewable electricity production in Sweden: Accounting for the effects of COVID-19

The COVID-19 pandemic has made a significant disruption in the renewable industry, and the effects will last longer. In this context, understanding how and which specific renewable power got affected due to this crisis is of crucial importance. This study examines the nexus between COVID-19 and Sweden's renewable electricity production from three sources of energy such as nuclear, solar, and wind, where the data ranges from January 1, 2019, to February 17, 2021. Since this study compares the period before and during the pandemic event, the study uses Air Quality Index as a measure of COVID-19 induced event and thus study the linkage between air quality and electricity production from three types of renewable energy. To analyse the above issue, several advanced techniques such as Wavelet Power Spectrum, Wavelet Coherence, Partial and Multiple Wavelet Coherence have been applied. The findings from the Wavelet Coherence approach demonstrate that COVID-19 has disrupted the linkage between wind energy generation and air quality, while the disruption in the case of solar and nuclear electricity generation has been minimal. Moreover, solar energy generation and air pollution both negatively affect each other, implying the need to generate solar power as well as reduce the level of air pollution in Sweden. In light of the above findings, the study discusses possible policy actions the country can take to fulfil its renewable development goals.

A governing equation for rotor and wavelet number in human clinical ventricular fibrillation: Implications for sudden cardiac death

BACKGROUND

Ventricular fibrillation (VF) is characterized by multiple wavelets and rotors. No equation to predict the number of rotors and wavelets observed during fibrillation has been validated in human VF.

OBJECTIVE

The purpose of this study was to test the hypothesis that a single equation derived from a Markov M/M/∞ birth-death process could predict the number of rotors and wavelets occurring in human clinical VF.

METHODS

Epicardial induced VF (256-electrode) recordings obtained from patients undergoing cardiac surgery were studied (12 patients; 62 epochs). Rate constants for phase singularity (PS) (which occur at the pivot points of rotors) and wavefront (WF) formation and destruction were derived by fitting distributions to PS and WF interformation and lifetimes. These rate constants were combined in an M/M/∞ governing equation to predict the number of PS and WF in VF episodes. Observed distributions were compared to those predicted by the M/M/∞ equation.

RESULTS

The M/M/∞ equation accurately predicted average PS and WF number and population distribution, demonstrated in all epochs. Self-terminating episodes of VF were distinguished from VF episodes requiring termination by a trend toward slower PS destruction, slower rates of PS formation, and a slower mixing rate of the VF process, indicated by larger values of the second largest eigenvalue modulus of the M/M/∞ birth-death matrix. The longest-lasting PS (associated with rotors) had shorter interactivation time intervals compared to shorter-lasting PS lasting <150 ms (∼1 PS rotation in human VF).

CONCLUSION

The M/M/∞ equation explains the number of wavelets and rotors observed, supporting a paradigm of VF based on statistical fibrillatory dynamics.

Fully automated unified prognosis of Covid-19 chest X-ray/CT scan images using Deep Covix-Net model

SARS-COV2 (Covid-19) prevails in the form of multiple mutant variants causing pandemic situations around the world. Thus, medical diagnosis is not accurate. Although several clinical diagnostic methodologies have been introduced hitherto, chest X-ray and computed tomography (CT) imaging techniques complement the analytical methods (for instance, RT-PCR) to a certain extent. In this context, we demonstrate a novel framework by employing various image segmentation models to leverage the available image databases (9000 chest X-ray images and 6000 CT scan images). The proposed methodology is expected to assist in the prognosis of Covid-19-infected individuals through examination of chest X-rays and CT scans of images using the Deep Covix-Net model for identifying novel coronavirus-infected patients effectively and efficiently. The slice of the precision score is analysed in terms of performance metrics such as accuracy, the confusion matrix, and the receiver operating characteristic curve. The result leans on the database obtainable in the GitHub and Kaggle repository, conforming to their endorsed chest X-ray and CT images. The classification performances of various algorithms were examined for a test set with 1800 images. The proposed model achieved a 96.8% multiple-classification accuracy among Covid-19, normal, and pneumonia chest X-ray databases. Moreover, it attained a 97% accuracy among Covid-19 and normal CT scan images. Thus, the proposed mechanism achieves the rigorousness associated with the machine learning technique, providing rapid outcomes for both training and testing datasets.

Testing dependence patterns of energy consumption with economic expansion and trade openness through wavelet transformed coherence in top energy-consuming countries

Economic growth and trade openness are closely linked with energy consumption and hence have environmental consequences. Many studies have investigated the relationship between these variables. Two weaknesses in empirical literature on energy-growth nexus are prominent. First majority of the studies are conducted on different groups of countries; however, no study has focused the top energy-consuming countries despite their immense importance in the context of energy-growth nexus. Second, this literature cannot simultaneously capture time and frequency domains, short- and long-run dependence, and lagging and leading effects among the variables. Furthermore, environmental impacts of increased energy consumption emerging from trade base economic growth are less studied. This study employs wavelet transformed coherence method to examine dependence partners of energy consumption with economic expansion and trade openness in top 10 energy-consuming countries. This methodology avoids the unrealistic assumption of stationarity of the variables due to favorable scaling tool and unveils the time frequency dependence among variables with more reliability as it accounts for the seasonality, cycles, or trends extracted from the transformation change over time. Furthermore, this technique has the novelty to handle data when its transformation from one-dimensional to bi-dimensional time-frequency sphere is allowed. Findings reveal a positive influence of economic growth and trade on energy consumption in many countries. The wavelet transformed coherence indicates short-run coherence among energy consumption and economic growth of all the top 10 energy-consuming countries. Long-run dependence among energy consumption and economic growth exists in case of China, India, Brazil, and South Korea with mostly leading role of energy consumption over economic growth. The findings of the study reiterate the importance of energy consumption in the development of these economies and suggest that energy policies aimed at improving efficiency in the production and consumption of energy will not hurt economic growth.

Construction of wavelet dictionaries for ECG modeling

Technical details, algorithms, and MATLAB implementation for a method advanced in the paper ``Wavelet Based Dictionaries for Dimensionality Reduction of ECG Signals'', are presented. This work aims to be the companion of that publication, in which an adaptive mathematical model for a given ECG record is proposed. The method comprises the following building blocks.(i)

Construction of a suitable redundant set, called 'dictionary', for decomposing an ECG signal as a superposition of elementary components, called 'atoms', selected from that dictionary.

(ii)

Implementation of the greedy strategy Optimized Orthogonal Matching Pursuit (OOMP) for selecting the atoms intervening in the signal decomposition.

This paper gives the details of the algorithms for implementing stage (i), which is not fully elaborated in the previous publication. The proposed dictionaries are constructed from known wavelet families, but translating the prototypes with a shorter step than that corresponding to a wavelet basis. Stage (ii) is readily implementable by the available function OOMP.•

The use of the software and the power of the technique is illustrated by reducing the dimensionality of ECG records taken from the MIT-BIH Arrhythmia Database.

•

The MATLAB software has been made publicly available on a dedicated website.

•

We provide the explanations, algorithms and software for the construction of scaling functions and wavelet prototypes for 17 different wavelet families. The procedure is designed to allow for straightforward extension of the software by the inclusion of additional options for the wavelet families.

Does green financing help to improve environmental & social responsibility? Designing SDG framework through advanced quantile modelling

Striving to achieve the Sustainable Development Goals (SDGs), countries are increasingly embracing a sustainable financing mechanism via green bond financing. Green bonds have attracted the attention of the industrial sector and policymakers, however, the impact of green bond financing on environmental and social sustainability has not been confirmed. There is no empirical evidence on how this financial product can contribute to achieving the goals set out in Agenda 2030. In this study, we empirically analyze the impact of green bond financing on environmental and social sustainability by considering the S&P 500 Global Green Bond Index and S&P 500 Environmental and Social Responsibility Index, from October 1, 2010 to 31st July 2020 using a combination of Quantile-on-Quantile Regression and Wavelet Multiscale Decomposition approaches. Our results reveal that green financing mechanisms might have gradual negative transformational impacts on environmental and social responsibility. Furthermore, we attempt to design a policy framework to address the relevant SDG objectives.

Accelerated free-breathing 3D whole-heart magnetic resonance angiography with a radial phyllotaxis trajectory, compressed sensing, and curvelet transform

PURPOSE

To develop and validate an accelerated free-breathing 3D whole-heart magnetic resonance angiography (MRA) technique using a radial k-space trajectory with compressed sensing and curvelet transform.

METHOD

A 3D radial phyllotaxis trajectory was implemented to traverse the centerline of k-space immediately before the segmented whole-heart MRA data acquisition at each cardiac cycle. The k-space centerlines were used to correct the respiratory-induced heart motion in the acquired MRA data. The corrected MRA data were then reconstructed by a novel compressed sensing algorithm using curvelets as the sparsifying domain. The proposed 3D whole-heart MRA technique (radial CS curvelet) was then prospectively validated against compressed sensing with a conventional wavelet transform (radial CS wavelet) and a standard Cartesian acquisition in terms of scan time and border sharpness.

RESULTS

Fifteen patients (females 10, median age 34-year-old) underwent 3D whole-heart MRA imaging using a standard Cartesian trajectory and our proposed radial phyllotaxis trajectory. Scan time for radial phyllotaxis was significantly shorter than Cartesian (4.88 ± 0.86 min. vs. 6.84 ± 1.79 min., P-value = 0.004). Radial CS curvelet border sharpness was slightly lower than Cartesian and, for the majority of vessels, was significantly better than radial CS wavelet (P-value < 0.050).

CONCLUSION

The proposed technique of 3D whole-heart MRA acquisition with a radial CS curvelet has a shorter scan time and slightly lower vessel sharpness compared to the Cartesian acquisition with radial profile ordering, and has slightly better sharpness than radial CS wavelet. Future work on this technique includes additional clinical trials and extending this technique to 3D cine imaging.

A fast wavelet-based functional association analysis replicates several susceptibility loci for birth weight in a Norwegian population

Background

Birth weight (BW) is one of the most widely studied anthropometric traits in humans because of its role in various adult-onset diseases. The number of loci associated with BW has increased dramatically since the advent of whole-genome screening approaches such as genome-wide association studies (GWASes) and meta-analyses of GWASes (GWAMAs). To further contribute to elucidating the genetic architecture of BW, we analyzed a genotyped Norwegian dataset with information on child’s BW (N=9,063) using a slightly modified version of a wavelet-based method by Shim and Stephens (2015) called WaveQTL.

Results

WaveQTL uses wavelet regression for regional testing and offers a more flexible functional modeling framework compared to conventional GWAS methods. To further improve WaveQTL, we added a novel feature termed “zooming strategy” to enhance the detection of associations in typically small regions. The modified WaveQTL replicated five out of the 133 loci previously identified by the largest GWAMA of BW to date by Warrington et al. (2019), even though our sample size was 26 times smaller than that study and 18 times smaller than the second largest GWAMA of BW by Horikoshi et al. (2016). In addition, the modified WaveQTL performed better in regions of high LD between SNPs.

Conclusions

This study is the first adaptation of the original WaveQTL method to the analysis of genome-wide genotypic data. Our results highlight the utility of the modified WaveQTL as a complementary tool for identifying loci that might escape detection by conventional genome-wide screening methods due to power issues. An attractive application of the modified WaveQTL would be to select traits from various public GWAS repositories to investigate whether they might benefit from a second analysis.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-021-07582-6).

Stochastic convergence in carbon emissions based on a new Fourier-based wavelet unit root test

Most of the existing studies on stochastic convergence of emission have not adequately considered smooth structural changes. The primary purpose of this paper is to examine the validity of stochastic convergence at different income levels by recently proposed Fourier-based wavelet augmented Dickey-Fuller test with smooth shifts. Empirical results can be summed up as follows: (i) carbon emission per capita follows the stationarity process in 35 high-income countries, while carbon emission per capita follows the stationarity process in 27 upper-middle-income countries; (ii) besides, carbon emission per capita follows stationarity process in 30 lower-middle-income countries, while carbon emission per capita follows stationarity process in 13 low-income countries; (iii) in light of these findings, it can be said that stochastic convergence among different income groups is valid. The implications of the empirical findings for environmental planning and management are discussed in the body of the paper.

Multiple sclerosis lesion segmentation from brain MRI using U-Net based on wavelet pooling

PURPOSE

The purpose of this work is to segment multiple sclerosis (MS) lesions in magnetic resonance imaging (MRI) images, in which lesions in different sizes are segmented with appropriate accuracy. Automated segmentation as a powerful tool can assist professionals to increase the accuracy of disease diagnosis and its level of progression.

METHODS

We present a deep neural network based on the U-Net architecture in which wavelet transform-based pooling replaces max pooling. In the first part of the network, the wavelet transform is used, and in the second part, it's inverse. In addition to decomposing the input image and reducing its size, the wavelet transform highlights sharp changes in the image and better describes local features. This transform has the multi-resolution characteristic, so its use provides improvement in the detection of lesions of different sizes and segmentation.

RESULTS

The results of this study show that the proposed method has a better Dice similarity coefficient (DSC) value compared to the max pooling and average pooling methods.

CONCLUSION

The proposed method has better results for segmenting MS lesions of different sizes in MRI images than the max and average pooling methods and other methods studied.

A method to test weak-form market efficiency from sectoral indices of the WAEMU stock exchange: A wavelet analysis

This study assesses the efficiency of the West African Economic and Monetary Union (WAEMU) regional stock exchange using daily data on its seven (7) sectoral indices from December 31, 2013, to January 4, 2019. To this end, we analyze the market structure and calculate the generalized Hurst index by using the discrete wavelet transformation (DWT) and wavelet leader transformation (WLT) approaches. Our conclusions can be summarized as follows: first, this study highlights the multifractal nature of the WAEMU stock market. Second, the Hurst generalized index reveals a persistent or nonpersistent process depending on the sector, according to the q chosen or the method used (DWT or WLT). The dynamics of the indices reveal the characteristics of short memory or, in some cases, long memory, and the efficient market hypothesis is rejected.

Discrimination of red porgy Pagrus pagrus (Sparidae) potential stocks in the south-western Atlantic by otolith shape analysis

Otolith shape analysis is a powerful method for fish stock identification. We compared the otolith shape of Pagrus pagrus (Linnaeus 1758) along with its distribution in four south-western Atlantic regions where it is commercially fished: Rio de Janeiro, Rio Grande do Sul in southern Brazil, the Argentine-Uruguayan Common Fishing Zone (UA) and the Argentinian Exclusive Fishing Zone (AR). Otolith shapes were compared by Elliptical Fourier and Wavelet coefficients among specimens in a size range with similar otoliths, morphometric parameters and ages. Four potential stocks were identified: one in the AR, a second along the UA which included specimens from southern Brazil with well-marked opaque bands in its otoliths (MRS), the third in southern Brazil with faint or absent opaque bands in its otoliths (FRS) and the fourth along Rio de Janeiro. The difference in the otolith shape among regions followed differences reported using other stock identification techniques. The similarity between otoliths from UA and MRS (ANOVA-like, P > 0.01) can be explained by seasonal short-range migrations. Otoliths shape differences between MRS and FRS (ANOVA-like, P < 0.01) suggest that P. pagrus does not form a homogeneous group in southern Brazil.

Cardiotocography signal abnormality classification using time-frequency features and Ensemble Cost-sensitive SVM classifier

BACKGROUND

Cardiotocography (CTG) signal abnormality classification plays an important role in the diagnosis of abnormal fetuses. This classification problem is made difficult by the non-stationary nature of CTG and the dataset imbalance. This paper introduces a novel application of Time-frequency (TF) features and Ensemble Cost-sensitive Support Vector Machine (ECSVM) classifier to tackle these problems.

METHODS

Firstly, CTG signals are converted into TF-domain representations by Continuous Wavelet Transform (CWT), Wavelet Coherence (WTC), and Cross-wavelet Transform (XWT). From these representations, a novel image descriptor is used to extract the TF features. Then, the linear feature is derived from the time-domain representation of the CTG signal. The linear and TF features are fed to the ECSVM classifier for prediction and classification of fetal outcome.

RESULTS

The TF features show the significant difference (p-value<0.05) in distinguishing abnormal CTG signals, but not for traditional nonlinear features. In ECSVM abnormality classification, using only linear features, the sensitivity, specificity, and quality index are 59.3%, 78.3%, and 68.1%, respectively, whereas more effective results (sensitivity: 85.2%, specificity: 66.1%, and quality index: 75.0%) are obtained using a combination of linear and TF features, with a performance improvement index of 10.1%. Especially, the area under the receiver operating characteristic curve (0.77 vs. 0.64) is significantly increased with the ECSVM vs. SVM.

CONCLUSION

Our method can greatly improve the classification results, especially for sensitivity. It improves the true positive rate of CTG abnormality classification and reduces the false positive rate, which may help detect and treat abnormal fetuses during labor.

Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis

We apply wavelet methods to daily data of COVID-19 world deaths and daily Bitcoin prices from 31th December 2019 to 29th April 2020. We find, especially for the period post April 5, that levels of COVID-19 caused a rise in Bitcoin prices. We contribute to the fast-growing body of work on the financial impacts of COVID-19, as well as to ongoing consideration of whether Bitcoin is a safe haven investment. Our results should be of great interest to both scholars and policy makers, as well as investment professionals interested in the financial implications of both COVID-19 and cryptocurrencies.

Time-frequency analysis of muscle activation patterns in people with chronic ankle instability during Landing and cutting tasks

BACKGROUND

People with chronic ankle instability (CAI) exhibit neuromuscular deficits. Previous studies, however, only investigated magnitudes of muscle activation and not the time-frequency domain.

RESEARCH QUESTION

Do people with CAI exhibit differences in muscle activation patterns in the time-frequency domain during landing, anticipated cutting, and unanticipated cutting compared to matched controls?

METHODS

Eleven people with CAI and eleven healthy matched controls (CON) performed landing, anticipated cutting, and unanticipated cutting as surface EMG of the lateral gastrocnemius, medial gastrocnemius, fibularis longus, soleus, and tibialis anterior were recorded. The time-frequency domain of surface EMG data was analyzed with wavelet transformations and principal component analysis (PCA), PC scores were compared across group, task, and muscle with three-way ANOVAs.

RESULTS

The PCA extracted two PCs that captured the overall magnitude (PC1) of wavelet intensities across the time-frequency domain and a shift among the range of frequencies (PC2) where wavelet intensities were most prominent. A main effect for group indicated that people with CAI demonstrated smaller (p = 0.009) PC1 scores than people in the CON group across all muscles and tasks. An interaction between group and task indicated that people in the CAI group exhibited smaller (p = 0.041) PC2 scores than people in the CON group during only anticipated cutting.

SIGNIFICANCE

People with CAI exhibited neuromuscular deficits in the time-frequency domain of EMG during dynamic tasks. These deficits appear to reflect a neuromuscular strategy characterized by the recruitment of fewer motor units in ankle muscles regardless of task, and an inability to scale the recruitment of motor units in the frequency domain in response to different task demands. Rehabilitation for people with CAI should consider that this population exhibits differences in neuromuscular control that exist not only in the overall magnitudes, but also in the time-frequency domain, of muscle activation patterns.

The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection

COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19. And new methods have been searching for determination of the COVID-19. In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out. Chest X-ray images are used as input to the proposed approach. As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective. To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images. Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications. The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study. A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works. The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision. As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out. To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered. For deep feature extraction, pretrained, ResNet50 model is employed. For classification of the deep features, the Support Vector Machines (SVM) classifier is used. The ResNet50 model is also used in the fine-tuning. The experimental works show that multiresolution approaches produced better performance than the deep learning approaches. Especially, Shearlet transform outperformed at all. 99.29% accuracy score is obtained by using Shearlet transform.

A Multi Record Based Artificial Near Fault Ground Motion Generation Method

Near fault ground motion is a kind of ground motion with great damage, which is difficult to simulate. The amplitude and frequency functions of near fault ground motion vary greatly with time. When a single ground motion is used for simulation, large errors can easily occur. A method for generating near fault ground motion based on a set of actual ground motions is presented. In order to evaluate these artificially near fault ground motions, artificial ground motions and corresponding natural ground motions are input into the finite element analysis program to obtain the response of the structure. It is found that these artificial near fault ground motions are good simulations of these actual ground motions. The method of near fault ground motion can make up for the shortage of near fault ground motion.

•

Near Fault ground motions collected from the same earthquake with similar site characteristics are taken as sample group. Each ground motion in the sample group is considered as a record of random event.

•

Because this method is based on multi ground motion samples, this method can take account of the variability of each ground motion sample on the premise of considering the earthquake characteristics, then the generated ground motion are more close to the real one.

•

The wavelet technique and random vibration theory are applied to the analysis and simulation of ground motion. Because of the application of these two technologies, the simulation efficiency and accuracy of this method are improved.

d-serine regulation of the timing and architecture of the inspiratory burst in neonatal mice

d-serine, released from mouse medullary astrocytes in response to increased CO₂ levels, boosts the respiratory frequency to adapt breathing to physiological demands. We analyzed in mouse neonates, the influence of d-serine upon inspiratory/expiratory durations and the architecture of the inspiratory burst, assessed by pwelch's power spectrum density (PSD) and continuous wavelet transform (CWT) analyses. Suction electrode recordings were performed in slices from the ventral respiratory column (VRC), site of generation of the respiratory rhythm, and in brainstem-spinal cord (en bloc) preparations, from the C5 ventral roots, containing phrenic fibers that in vivo innervate and drive the diaphragm, the main inspiratory muscle. In en bloc and slice preparations, d-serine (100 μM) reduced the expiratory, but not the inspiratory duration, and increased the frequency and the regularity of the respiratory rhythm. In en bloc preparations, d-serine (100 μM) also increased slightly the amplitude of the integrated inspiratory burst and the area under the curve of the integrated inspiratory burst, suggesting a change in the recruitment or the firing pattern of neurons within the burst. Time-frequency analyses revealed that d-serine changed the burst architecture of phrenic roots, widening their frequency spectrum and shifting the position of the core of firing frequencies towards the onset of the inspiratory burst. At the VRC, no clear d-serine induced changes in the frequency-time domain could be established. Our results show that d-serine not only regulates the timing of the respiratory cycle, but also the recruitment strategy of phrenic motoneurons within the inspiratory burst.

COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach

In this paper, we analyze the connectedness between the recent spread of COVID-19, oil price volatility shock, the stock market, geopolitical risk and economic policy uncertainty in the US within a time-frequency framework. The coherence wavelet method and the wavelet-based Granger causality tests applied to US recent daily data unveil the unprecedented impact of COVID-19 and oil price shocks on the geopolitical risk levels, economic policy uncertainty and stock market volatility over the low frequency bands. The effect of the COVID-19 on the geopolitical risk substantially higher than on the US economic uncertainty. The COVID-19 risk is perceived differently over the short and the long-run and may be firstly viewed as an economic crisis. Our study offers several urgent prominent implications and endorsements for policymakers and asset managers.

Identifying multiscale spatio-temporal patterns in human mobility using manifold learning

When, where and how people move is a fundamental part of how human societies organize around every-day needs as well as how people adapt to risks, such as economic scarcity or instability, and natural disasters. Our ability to characterize and predict the diversity of human mobility patterns has been greatly expanded by the availability of Call Detail Records (CDR) from mobile phone cellular networks. The size and richness of these datasets is at the same time a blessing and a curse: while there is great opportunity to extract useful information from these datasets, it remains a challenge to do so in a meaningful way. In particular, human mobility is multiscale, meaning a diversity of patterns of mobility occur simultaneously, which vary according to timing, magnitude and spatial extent. To identify and characterize the main spatio-temporal scales and patterns of human mobility we examined CDR data from the Orange mobile network in Senegal using a new form of spectral graph wavelets, an approach from manifold learning. This unsupervised analysis reduces the dimensionality of the data to reveal seasonal changes in human mobility, as well as mobility patterns associated with large-scale but short-term religious events. The novel insight into human mobility patterns afforded by manifold learning methods like spectral graph wavelets have clear applications for urban planning, infrastructure design as well as hazard risk management, especially as climate change alters the biophysical landscape on which people work and live, leading to new patterns of human migration around the world.

Spatial and temporal clustering of patients hospitalized with laboratory-confirmed influenza in the United States

BACKGROUND

Timing of influenza spread across the United States is dependent on factors including local and national travel patterns and climate. Local epidemic intensity may be influenced by social, economic and demographic patterns. Data are needed to better explain how local socioeconomic factors influence both the timing and intensity of influenza seasons to result in national patterns.

METHODS

To determine the spatial and temporal impacts of socioeconomics on influenza hospitalization burden and timing, we used population-based laboratory-confirmed influenza hospitalization surveillance data from the CDC-sponsored Influenza Hospitalization Surveillance Network (FluSurv-NET) at up to 14 sites from the 2009/2010 through 2013/2014 seasons (n = 35,493 hospitalizations). We used a spatial scan statistic and spatiotemporal wavelet analysis, to compare temporal patterns of influenza spread between counties and across the country.

RESULTS

There were 56 spatial clusters identified in the unadjusted scan statistic analysis using data from the 2010/2011 through the 2013/2014 seasons, with relative risks (RRs) ranging from 0.09 to 4.20. After adjustment for socioeconomic factors, there were five clusters identified with RRs ranging from 0.21 to 1.20. In the wavelet analysis, most sites were in phase synchrony with one another for most years, except for the H1N1 pandemic year (2009-2010), wherein most sites had differential epidemic timing from the referent site in Georgia.

CONCLUSIONS

Socioeconomic factors strongly impact local influenza hospitalization burden. Influenza phase synchrony varies by year and by socioeconomics, but is less influenced by socioeconomics than is disease burden.

Efficient de-noising of high-resolution fMRI using local and sub-band information

BACKGROUND

High-resolution fMRI, useful for accurate brain mapping, suffers from low functional sensitivity at a reasonable acquisition time. Conventional smoothing techniques although reduce the noise and boost the sensitivity, but degrade the spatial resolution of fMRI.

NEW METHODS

We propose a novel spatial de-noising technique to increase sensitivity while preserving the boundaries of active regions in the high-resolution fMRI. A modified version of PCA that utilizes adjacent voxels information (LPCA) is first suggested for de-noising. This technique is then further empowered by its application to wavelet sub-bands (WLPCA).

RESULTS

Proposed techniques were assessed on both simulated and experimental data. Identifiablity index was calculated for evaluation of the denoising on the simulated data. Maximum and mean z-scores along with LAE and SSIM were reported on experimental data for two presented techniques as well as Guassian smoothing. WLPCA outperformed other techniques in Identifiablity index, for simulation, and in preserving maximum z-score, for experimental study.

COMPARISON WITH EXISTING METHODS

The presented technique was developed to simultaneously suppress the noise and preserve the boundaries of active areas against leakage. For first aim, its achievable mean z-score was compared to conventional Gaussian. For second aim, its maximum z-score was compared to that of no-smoothing. While Gaussian and no-smoothing can work fine with only one measure, WLPCA was able to improve both measures concurrently.

CONCLUSIONS

The local PCA based methods, and in particular WLPCA, is an effective noise reduction step that preserves the spatial resolution by preventing activity leakage of high-resolution fMRI data.

Introducing the Hybrid "K-means, RLS" Learning for the RBF Network in Obstructive Apnea Disease Detection using Dual-tree Complex Wavelet Transform Based Features

Apnea is one of the deadliest diseases that can be prevented and cured if it is detected in time. In this paper, we propose a precise method for early detection of the obstructive sleep apnea (OSA) disease using the latest feature selection and extraction methods. The feature selection in this paper is based on the Dual tree complex wavelet (DT-CWT) coefficients of the ECG signals of several patients. The feature extraction from these coefficients is done using frequency and time techniques. The Feature selection is done using the spectral regression discriminant analysis (SRDA) algorithm and the classification is performed using the hybrid RBF network. A hybrid RBF neural network is introduced in this paper for detecting apnea that is much less computationally demanding than the previously presented SVM networks. Our findings showed a 3 percent improvement in the detection and at least a 30 percent reduction in the computational complexity in comparison with methods that have been presented recently.

Analysis of velocity calculation methods of laser-induced surface acoustic wave

The key to measuring residual stress by surface acoustic wave method is the accurate measurement of velocity. In this paper, the velocity of laser-induced broadband surface acoustic wave is studied, and three velocity calculation methods of surface acoustic wave, time domain method, phase method and wavelet method are compared. The calculation error of the time domain method under the condition of dispersion is analyzed. A recursive method for calculating phase difference is proposed to improve the efficiency of phase method. The simulated surface acoustic waves are used to compare the phase method and wavelet method under the conditions of attenuation and dispersion. Compared with the wavelet method, the phase method cannot distinguish the time when the frequency band appears, and the velocity calculation of adjacent frequency points is related, while the wavelet method is independent of each other. The wavelet method can improve the calculation accuracy of the velocity curve by interpolating the original data. After interpolation, the trend of curve is more obvious, and the fitting error is greatly reduced.

Comprehensive climate factor characteristics and quantitative analysis of their impacts on grain yields in China's grain-producing areas

Climate change elements are important indicators for assessing the impact of climate change on the agricultural economy. A Comprehensive Climate Factor (CCF) that is composed of three indicators, growing season mean temperature, precipitation and sunshine hours indicators was developed. These indicators are aggregated into a single index that is a measure of the sensitivity of regionally integrated climate change. This paper uses this factor to explore the integrated climate variations over China's grain-producing areas in 1981–2015, divide the areas into climate change-sensitive zones, and quantitatively assess the impact intensity of CCF variation on grain yield. The results indicate that the growing season mean CCF basically increased in most grain-producing areas. The climatic tendency of the North plate is greater than that of the South plate, reaching 0.52 decade⁻¹, and the South plate has a quasi-4a periodic variation. The patterns of the impact of climate change on grain yield show that the impact intensity of climate change gradually decreased in each decade (from 0.25 to 0.2) and was stronger in the southwest than in the northeast. This research can be applied to improve the accuracy of economic-climate model simulations and predictions and to provide a theoretical reference and scientific support for assessing the impact and risk of climate change.