Regular Components Research Articles

Fiscal consolidation plans with underground economy

AbstractFiscal consolidation literature often neglects that there are economies characterized by sizable underground sectors, with their mechanisms of response to fiscal policy shocks. Therefore, reliable analyses of fiscal consolidation plans call for the decomposition of Gross Domestic Product in its regular and hidden components. We investigate fiscal consolidation effects in the context of tax evasion for the Italian economy. Results show that a temporary cut in public spending associated with a permanent drop in tax rate yet entails tax evasion reduction. The main underlying mechanism is the strong responsiveness of the underground sector, implying a reallocation of resources toward the regular sector.

Constraining gravitational-wave backgrounds from conversions into photons in the Galactic magnetic field

High-frequency gravitational waves (f≳1 MHz) may provide a unique signature for the existence of exotic physics. The lack of current and future gravitational-wave experiments sensitive at those frequencies leads to the need of employing different indirect techniques. Notably, one of the most promising ones is constituted by graviton-photon conversions in magnetic fields. In this work, we focus on conversions of a gravitational-wave background into photons inside the Milky Way magnetic field, taking into account the state-of-the-art models for both regular and turbulent components. We discuss how graviton-to-photon conversions may lead to imprints in the cosmic photon background spectrum in the range of frequencies f∼109–1026 Hz, where the observed photon flux is widely explained by astrophysics emission models. Hence, the absence of any significant evidence for a diffuse photon flux induced by graviton-photon conversions allows us to set stringent constraints on the gravitational-wave strain hc, strengthening current astrophysical bounds by ∼1–2 orders of magnitude in the whole range of frequencies considered. Published by the American Physical Society 2024

Advances in Multiscale Modeling and Mechanical Properties Characterization of 3D‐Braided Composites

The 3D‐braided composites have been widely used in aerospace and other fields due to the excellent mechanical properties, designability, and resistance to delamination. Accurately analyzing and evaluating mechanical properties of braided composite structures is the key, which successfully designs related structural components. However, 3D‐braided composites have complex internal meso–microscopic structures, and directly establishing a solid structure model to predict mechanical behavior poses certain challenges. The multiscale research methods are a type of approach that studies the cross‐scale structural characteristics at macro–meso–microscopic scales and couples relevant scales into a whole. Its emergence provides the possibility for evaluating the overall structural performance of 3D‐braided composites. In this article, first, the multiscale analysis models of 3D‐multidirectional‐braided composites are reviewed, and then the theoretical and numerical research progress on the static and dynamic multiscale mechanical performance of regular components of 3D‐braided composites is summarized. In addition, due to the distortion and variation of geometric shapes of 3D‐braided composites on the meso–microscopic structure; therefore, also in this article, the research progress on multiscale mechanical performance characteristics of special‐shaped components is summarized. Finally, the key problems in the multiscale research of 3D‐braided composites are presented.

Hydrocarbon-Oxidizing Bacteria of the Bottom Ecotopes of the Barents and Pechora Seas

Microorganisms capable of degrading hydrocarbons are regular components of natural microbial communities and play an important role in self-purification of marine environments from oil contamination. High-throughput sequencing of the 16S rRNA gene V4 variable region was used to analyze microbial communities of the Barents and Pechora seas and of the microcosms with a spectrum of hydrocarbon substrates: oil, n-nonane, n-undecane, and phenanthrene. The Barents Sea communities of hydrocarbon-oxidizing microorganisms were characterized by predominance of the genera Pseudoalteromonas, Pseudomonas, Porticoccus, and Oleispira, while those of the Pechora Sea contained members of the genera Rhodococcus, Dietzia, Sphingorhabdus, and Hyphomonas. Pure cultures of these microorganisms were shown to utilize the major oil hydrocarbons: n-alkanes, cycloalkanes, and aromatic compounds.

Dual‐channel observer design for composite disturbances based on characteristic compensation

AbstractThis paper presents a non‐coupled structure of characteristic compensation dual‐channel observer (CCDCO) for handling systems subjected to composite disturbances. There exist an outer disturbance compensation channel (ODCC) and an inner disturbance compensation channel (IDCC) in the design strategy. Specifically, in the ODCC, the proposed frequency characteristic compensation observer (FCCO) extracts and removes the regular periodic components from the composite disturbances, where the estimation error preserves irregular characteristics. Thus, the estimation error of FCCO and irregular parts are treated as lumped terms estimated via compensation function observer (CFO) in the IDCC. As a solving skill for composite disturbances estimation, CCDCO has two important advantages over previous designs. First, the proposed non‐coupled structure explicitly distinguishes disturbance characteristics according to a priori information known or not. Second, by combining the benefits of the CFO and proposed FCCO, different types of disturbances can be estimated accordingly without coupling of each other. Simulation results on the robotic manipulator are provided to validate the effectiveness of the proposed method.

Heat load forecasting model considering two dimensional changes of time series

The regulation strategy of a district heating system is adjusted based on accurate heat load prediction, which not only effectively reduces energy consumption but also improves system efficiency and user comfort. In order to improve the accuracy of heat load forecasting, a heat load forecasting model considering the two-dimensional change of time series is introduced in this paper. Firstly, the original heat load data is denoised by SVMD decomposition, and several stationary and regular modal components are obtained. Then, three strategies were used to enhance the BWO algorithm, and the IBWO-TimesNet prediction model was established to extract the hidden information of time series data from a two-dimensional perspective. Finally, the prediction performance of the model is evaluated in detail through case analysis. The results show that MAE, RMSE and R2 of SVMD-IBWO-TimesNet model are 0.647, 1.190 and 99.1%, respectively. Compared with other mainstream prediction models, this model has higher prediction accuracy. In addition, even if the training samples are reduced, the SVMD-IBWO-TimesNet model can still effectively predict the heat load and has strong generalization ability. Therefore, the performance of the model is verified, which provides a reference for the accurate control of heat load. Practical application Heat load forecasting is a vital task particularly in relation to its impact on the management of building energy efficiency. The contribution of this paper is to provide a heat load forecasting model based on advanced algorithms and data analysis. This insight derived from advanced modelling will assist professionals in the pursuit of more accurate prediction of the heat needs of buildings, thereby optimizing the design and operation of heating systems. The practical application of this technology could save energy costs, reduce carbon emissions, and improve the comfort and sustainability of buildings.

Slow and steady: auditory features for discriminating animal vocalizations.

We propose that listeners can use temporal regularities - spectro-temporal correlations that change smoothly over time - to discriminate animal vocalizations within and between species. To test this idea, we used Slow Feature Analysis (SFA) to find the most temporally regular components of vocalizations from birds (blue jay, house finch, American yellow warbler, and great blue heron), humans (English speakers), and rhesus macaques. We projected vocalizations into the learned feature space and tested intra-class (same speaker/species) and inter-class (different speakers/species) auditory discrimination by a trained classifier. We found that: 1) Vocalization discrimination was excellent (> 95%) in all cases; 2) Performance depended primarily on the ~10 most temporally regular features; 3) Most vocalizations are dominated by ~10 features with high temporal regularity; and 4) These regular features are highly correlated with the most predictable components of animal sounds.

Carbon futures return forecasting: A novel method based on decomposition-ensemble strategy and Markov process

Accurate forecasting of carbon futures return is essential for global climate policy and financial markets, yet it remains a significant challenge due to the complex nature of these returns. This paper proposes a novel hybrid model for carbon futures return forecasting based on decomposition-ensemble strategy and Markov process. This model can effectively capture nonlinear, nonstationary and volatile features of carbon prices and provide accurate point and interval forecasts. We use the Moth–Flame Optimization (MFO) algorithm and the Average Maximum Envelope Entropy (AMEE) function to optimize the parameters of Variational Mode Decomposition (VMD), which is a data decomposition technique that can separate the regular components and the random temporal component (noise) in the time series. We use Support Vector Regression (SVR) models to forecast the regular subsequences obtained by VMD, and Markov processes to quantify and forecast the random temporal component. We combine these forecasts to obtain the final prediction results, which incorporate both the regular and random information in the data. The proposed model supports both point and interval predictions, which are derived by summing the results of all the sub-models. We conduct extensive experiments to validate our approach and compare it with other models. The results show that our model outperforms other models in terms of prediction accuracy, reliability, and economic value. Our model can provide significant economic value for carbon futures investors and help them make favorable decisions in investment.

Helical Gearbox Defect Detection with Machine Learning Using Regular Mesh Components and Sidebands.

The current paper presents helical gearbox defect detection models built from raw vibration signals measured using a triaxial accelerometer. Gear faults, such as localized pitting, localized wear on helical pinion tooth flanks, and low lubricant level, are under observation for three rotating velocities of the actuator and three load levels at the speed reducer output. The emphasis is on the strong connection between the gear faults and the fundamental meshing frequency GMF, its harmonics, and the sidebands found in the vibration spectrum as an effect of the amplitude modulation (AM) and phase modulation (PM). Several sets of features representing powers on selected frequency bands or/and associated peak amplitudes from the vibration spectrum, and also, for comparison, time-domain and frequency-domain statistical feature sets, are proposed as predictors in the defect detection task. The best performing detection model, with a testing accuracy of 99.73%, is based on SVM (Support Vector Machine) with a cubic kernel, and the features used are the band powers associated with six GMF harmonics and two sideband pairs for all three accelerometer axes, regardless of the rotation velocities and the load levels.

Mid-to-long Term Wind Power Forecasting Based on ARIMA-BP Combined Model

Background: Increasing the accuracy of the output power forecasting for wind power is helpful to the improvement of the reliability of power dispatching. Objective: This study aimed to improve the forecasting accuracy of mid-to-long term wind power. Methods: A mid-to-long term wind power forecasting based on ARIMA-BP combined model was proposed. The Empirical Mode Decomposition (EMD) was used to decompose the historical wind power series and obtain the Intrinsic Mode Function (IMF) and residual components, thereby obtaining more regular components. Then, the optimum feature set was obtained based on the minimum Redundancy Maximum Relevance (mRAR) to improve the prediction accuracy for feature extraction. After that, the high-frequency components were predicted using the Back Propagation (BP) neural network model, while the low-frequency components were predicted using the Autoregressive Integrated Moving Average model (ARIMA). Finally, the predicted components obtained were superimposed to deduce the final mid- and long-term wind power prediction results. Results: An analysis was conducted according to the actual data from a typical wind farm. After comparison, it was found that, after empirical mode decomposition and feature extraction analysis, the error of the intelligent combination algorithm based on the ARIMA-BP combined model was smaller than that using only the BP neural network or only the ARIMA. Conclusion: By means of actual data analysis, the effectiveness of the method proposed by the study for mid- and long-term wind power prediction was verified.

A speech enhancement model based on noise component decomposition: Inspired by human cognitive behavior

In multimedia intelligent systems, speech enhancement is commonly employed to improve the quality of speech signals, making them clearer and more natural. Current deep learning-based speech enhancement models typically treat noise as a unified entity and aim to separate it from the target speech. In this paper, inspired by the cognitive behavior of the human brain when observing noisy speech spectrograms, we decompose the spectral energy of noise into regular and random components. We propose an auxiliary-model-based speech enhancement framework that better suppresses noise components closely resembling speech features. Firstly, we introduce a voiceprint segmentation network (VSnet) that partitions noisy speech into voiceprint and non-voiceprint regions. Subsequently, we present a noise reconstruction network (NRnet) that utilizes noise information from non-voiceprint regions to reconstruct and suppress the regular noise components within the voiceprint region. Finally, we construct a combination of a model dedicated to suppressing random components (RANnet) and a speech enhancement model (SEnet), and train them synchronously. By sharing encoder parameters, SEnet is compelled to reduce the extraction of regular noise features from the original noisy speech, contributing to improving speech quality generated through the decoder. Experimental results on public Voickbank-DEMAND and DNS-challenge 2020 datasets demonstrate that our approach achieves state-of-the-art performance.

Hybrid Model of Natural Time Series with Neural Network Component and Adaptive Nonlinear Scheme: Application for Anomaly Detection

It is often difficult to describe natural time series due to implicit dependences and correlated noise. During anomalous natural processes, anomalous features appear in data. They have a nonstationary structure and do not allow us to apply traditional methods for time series modeling. In order to solve these problems, new models, adequately describing natural data, are required. A new hybrid model of a time series (HMTS) with a nonstationary structure is proposed in this paper. The HMTS has regular and anomalous components. The HMTS regular component is determined on the basis of an autoencoder neural network. To describe the HMTS anomalous component, an adaptive nonlinear approximating scheme (ANAS) is used on a wavelet basis. HMTS is considered in this investigation for the problem of neutron monitor data modeling and anomaly detection. Anomalies in neutron monitor data indicate negative factors in space weather. The timely detection of these factors is critically important. This investigation showed that the developed HMTS adequately describes neutron monitor data and has satisfactory results from the point of view of numeric performance. The MSE model values are close to 0 and errors are white Gaussian noise. In order to optimize the estimate of the HMTS anomalous component, the likelihood ratio test was applied. Moreover, the wavelet basis, giving the least losses during ANAS construction, was determined. Statistical modeling results showed that HMTS provides a high accuracy of anomaly detection. When the signal/noise ratio is 1.3 and anomaly durations are more than 60 counts, the probability of their detection is close to 90%. This is a high rate in the problem domain under consideration and provides solution reliability of the problem of anomaly detection in neutron monitor data. Moreover, the processing of data from several neutron monitor stations showed the high sensitivity of the HMTS. This shows the possibility to minimize the number of engaged stations, maintaining anomaly detection accuracy compared to the global survey method widely used in this field. This result is important as the continuous operation of neutron monitor stations is not always provided. Thus, the results show that the developed HMTS has the potential to address the problem of anomaly detection in neutron monitor data even when the number of operating stations is small. The proposed HMTS can help us to decrease the risks of the negative impact of space weather anomalies on human health and modern infrastructure.

A Novel Ensemble Machine Learning Model for Oil Production Prediction with Two-Stage Data Preprocessing

Petroleum production forecasting involves the anticipation of fluid production from wells based on historical data. Compared to traditional empirical, statistical, or reservoir simulation-based models, machine learning techniques leverage inherent relationships among historical dynamic data to predict future production. These methods are characterized by readily available parameters, fast computational speeds, high precision, and time–cost advantages, making them widely applicable in oilfield production. In this study, time series forecast models utilizing robust and efficient machine learning techniques are formulated for the prediction of production. We have fused the two-stage data preprocessing methods and the attention mechanism into the temporal convolutional network-gated recurrent unit (TCN-GRU) model. Firstly, the random forest (RF) algorithm is employed to extract key dynamic production features that influence output, serving to reduce data dimensionality and mitigate overfitting. Next, the mode decomposition algorithm, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is introduced. It employs a decomposition–reconstruction approach to segment production data into high-frequency noise components, low-frequency regular components and trend components. These segments are then individually subjected to prediction tasks, facilitating the model’s ability to capture more accurate intrinsic relationships among the data. Finally, the TCN-GRU-MA model, which integrates a multi-head attention (MA) mechanism, is utilized for production forecasting. In this model, the TCN module is employed to capture temporal data features, while the attention mechanism assigns varying weights to highlight the most critical influencing factors. The experimental results indicate that the proposed model achieves outstanding predictive performance. Compared to the best-performing comparative model, it exhibits a reduction in RMSE by 3%, MAE by 1.6%, MAPE by 12.7%, and an increase in R2 by 2.6% in Case 1. Similarly, in Case 2, there is a 7.7% decrease in RMSE, 7.7% in MAE, 11.6% in MAPE, and a 4.7% improvement in R2.

Ag nanoparticles decorated on Co3O4‐C derived from ZIF‐67: a recyclable catalyst for highly efficient reduction of nitrophenols

AbstractSilver‐based nanostructured composites as effective and stable catalysts were successfully designed as the catalyst to reduce the nitrophenols in aqueous solution at room temperature in the presence of NaBH4. The Co3O4‐C composites with regular morphology and adjustable components derived from ZIF‐67 in an air atmosphere at various temperatures (270, 300, and 350 °C) were chosen as a favorable platform to support silver nanoparticles. The acquired silver‐based nanocatalyst (Ag/Co3O4‐C) exhibited excellent catalytic performance towards the hydrogenation of nitrophenols (2‐NP, 3‐NP, and 4‐NP). The calculated rate constant (Kapp) towards the reduction of 4‐NP followed the order: Ag/Co3O4‐C‐300 (0.70 min−1)>Ag/Co3O4‐C‐270 (0.58 min−1)>Ag/Co3O4‐C‐350 (0.46 min−1). Ag/Co3O4‐C‐300 possessed the highest catalytic activity with no induction time during the reaction. What is more, Ag/Co3O4‐C‐300 could reduce 2‐NP and 3‐NP with the rate constant of 0.936 min−1 and 0.391 min−1, respectively. The catalytic activity was higher that many other metal‐based nanocomposites. The excellent catalytic performance was mainly attributed to the abundant active sites on the surface, and the interfacial synergistic effect between Ag and Co3O4‐C, which had a potential driving force for the hydrogenation reaction. In addition, the catalyst remained active even after recirculation experiments. The Ag‐based nanocomposites can be expected to be applied in industrial applications.

An ensemble multi-scale framework for long-term forecasting of air quality.

The significance of accurate long-term forecasting of air quality for a long-term policy decision for controlling air pollution and for evaluating its impacts on human health has attracted greater attention recently. This paper proposes an ensemble multi-scale framework to refine the previous version with ensemble empirical mode decomposition (EMD) and nonstationary oscillation resampling (NSOR) for long-term forecasting. Within the proposed ensemble multi-scale framework, we on one hand apply modified EMD to produce more regular and stable EMD components, allowing the long-range oscillation characteristics of the original time series to be better captured. On the other hand, we provide an ensemble mechanism to alleviate the error propagation problem in forecasts caused by iterative implementation of NSOR at all lead times and name it improved NSOR. Application of the proposed multi-scale framework to long-term forecasting of the daily PM2.5 at 14 monitoring stations in Hong Kong demonstrates that it can effectively capture the long-term variation in air pollution processes and significantly increase the forecasting performance. Specifically, the framework can, respectively, reduce the average root-mean-square error and the mean absolute error over all 14 stations by 8.4% and 9.2% for a lead time of 100 days, compared to previous studies. Additionally, better robustness can be obtained by the proposed ensemble framework for 180-day and 365-day long-term forecasting scenarios. It should be emphasized that the proposed ensemble multi-scale framework is a feasible framework, which is applicable for long-term time series forecasting in general.

Vectorial bent functions with non-weakly regular components

Vectorial bent functions with non-weakly regular components

Modeling the air pollution process using a novel multi-site and multi-scale method with adaptive utilization of spatio-temporal information

This study focuses on modeling air quality with an adaptive utilization of spatio-temporal information from multiple air quality monitoring stations under a multi-scale framework. To this end, it is necessary to consider different strategies to combine different methods to decompose the given series and to fuse multi-site information. Based on a systematic comparative analysis, we propose a novel multi-scale and multi-site modeling method named the multivariate empirical mode decomposition and spatial cosine-attention-based long short-term memory (MEMD-SCA-LSTM). The MEMD-SCA-LSTM first employs MEMD to decompose the multi-site air quality series into the scale-aligned components and then models the components at different scales. The high-frequency components are modeled by a novel SCA-LSTM, which employs LSTM with residual blocks to extract the temporal information and utilizes an attention mechanism based on the cosine similarity to adaptively extract interactions among different sites. Other relatively regular components are modeled by the LSTM. Empirical study on PM2.5 in Hong Kong has demonstrated the effectiveness of fusing multi-site information using the spatial attention (SA) mechanism under the multi-scale framework with MEMD. The proposed MEMD-SCA-LSTM can improve the one-day ahead modeling performance with the mean absolute error and the root mean square error reduced over 10%, compared to the baseline modeling methods. For the two-day and three-day ahead performance, the MEMD-SCA-LSTM is still the best one. Furthermore, by visualizing the attention weights, we illustrate that our proposed SCA-LSTM can overcome some limitations of the traditional attention mechanisms and that the attention weights exhibit more informative patterns which could be used to analysis the transport of air pollutant between sites. The proposed modeling method is a general method, which is feasible and applicable to other pollutants in other cities or regions.

FREE SURFACE TWO-DIMENSIONAL PERIODIC DISTURBANCES IN VARIOUS MODELS OF THE FLUID

The complete dispersion relations of a flat free surface periodic perturbations with a positive definite frequency and a complex wavenumber taking into account spatial attenuation in a viscous stratified charged liquid were obtained in a linear approximation for the first time by methods of the theory of singular perturbations. Regular components of the complete solution describe plane gravitational-capillary waves. Singular components characterize ligaments – thin flows that are absent in the model of an ideal medium. The obtained dispersion relations in extreme cases uniformly transform into known expressions for inviscid stratified, viscous homogeneous and ideal liquids. The calculated dependencies of the wavelength and thickness of the ligament, the group and phase velocity of the components on the frequency at different values of the media parameters are given.

The Method of Preprocessing of ECG Signals for Detection of Atrial and Ventricular Late Potentials

This article is aimed at analyzing and improving the methods of preprocessing ECG signals for the task of detecting low-amplitude regular components. This study analyzed the main advantages and disadvantages of existing ECG signal preprocessing methods for the detection of late ventricular and atrial potentials. Based on this analysis, a cardiac cycle averaging method was proposed in order to increase the accuracy of detection of late potentials by various algorithms and improve the quality of preprocessing of the ECG signal aimed at detection of low-amplitude components. The main feature of the proposed method is the division of a large number of cardiocycles for averaging into smaller aggregates (epochs), and the subsequent application of linear matrix decomposition to suppress irregular inclusions. Also, when dividing into epochs, it can be used overlapping. It can reduce the difference between epochs, and increase the number of cardiocycles for averaging. The use of this approach allows to minimize irregular inclusions in the ECG signal and increase the accuracy of the selection of low-amplitude late potentials. In addition, the division into epochs and overlapping makes possible to avoid blurring of low-amplitude high-frequency components during averaging as a result of heart rate variability, as well as to improve the quality of averaging with a reduced number of cardiocycles. To test the proposed method, various approaches were used to assess the ECG signal preprocessing. Mostly, we compared the cardiac cycles obtained as a result of different averaging algorithms and the proposed method with the template. To test the averaging method, an artificial ECG signal was developed with existing noise, late ventricular and atrial potentials, heart rate variability, and a high-amplitude component that occurs at a random location every two heartbeats. The template cardiac cycle was obtained from the original artificial signal without any distortion or noise. Firstly, we visually compared and evaluated different averaging methods with the template. Secondly, we calculated the similarity metrics of the late potentials on the averaged cardiac cycle with the late potentials on the template signal. Based on these metrics, the curves of dependence of the similarity values on the amplitude of late potentials on the ECG signal were calculated. Thirdly, we evaluated the impact of the proposed averaging method on the classification results of various machine learning algorithms on real ECG signals with available late potentials. The overall testing result showed that the proposed averaging method is able to reproduce the morphology of low-amplitude regular components by 10-30% more accurately and improve the classification accuracy by 5-12%.

Analysis of a numerical technique for a singularly perturbed parabolic convection–diffusion interface problem with time delay

In this article, we are interested in the numerical analysis of a singularly perturbed parabolic differential equation with time delay. The source term of the considered problem has discontinuities in the spatial variable along the interface , 0<d<1, and the diffusion coefficient is a small positive perturbation parameter. The problem's solution exhibits interior and boundary layers as the perturbation parameter approaches zero, which makes the problem challenging to establish the uniform convergence of any applied classical numerical techniques with respect to the perturbation parameter. The solution to the considered problem is decomposed into regular and singular components. Some appropriate a priori bounds on derivatives of these components have been given. The domain is discretized using Shishkin mesh in the spatial direction and uniform mesh in the time direction. On the mesh points which are not on the interface, the problem is discretized using a central-difference upwind scheme. Along the interface, the problem is discretized using an especial central-difference upwind scheme that uses the average value of the source term. The ε-uniform convergence analysis of the scheme is given using the decomposition of the solution. Some numerical experiments are conducted to corroborate the efficiency of the method.