Decomposition Results Research Articles

Improving the forecasting accuracy of interval-valued carbon price from a novel multi-scale framework with outliers detection: An improved interval-valued time series analysis mode

Accurate carbon price forecasting can provide policymakers with a reasonable basis for carbon pricing. Interval-valued carbon price forecasting could provide sufficient information compared with real-valued carbon price time series prediction. On the other hand, current interval-valued carbon price forecasting has major challenges including data complexity, outliers, and the selection of forecasting methods, which make the forecasting results with great uncertainty and instability. To address these issues, this paper proposes an interval-valued carbon price forecasting method based on new data processing techniques, and discusses the effects of different combinations of interval variables on the forecasting results. We first established interval complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and interval outlier detection method (I-ksigma) to reduce the data complexity and identify outliers. Then, various neural network models include interval multilayer perceptron (IMLP), multi-output support vector regression (MSVR), long short-term memory network (LSTM), gated recurrent unit neural network (GRU), and convolution neural network (CNN) are chosen to conduct combination forecasting on the interval sub-modes produced by ICEEMDAN. The final results are obtained by summing the interval sub-modes. Finally, taking the carbon trading price in Hubei as the research object, the results show that the developed forecasting framework is superior to all comparison models in forecasting precision and stability. Furthermore, different combinations of interval variables (CRM, Minmax, L + 2R, and U-2R) lead to different decomposition results and outlier detection results, which finally affect the prediction results.

One-Step Method for Material Quantitation Using In-Line Tomography With Single Scanning.

Quantitative technique based on In-line phase-contrast computed tomography with single scanning attracts more attention in application due to the flexibility of the implementation. However, the quantitative results usually suffer from artifacts and noise since the phase retrieval and reconstruction are independent ("two-step") without feedback from the original data. The work aims to investigate a method for material quantitation to improve the image quality of In-line tomography within single scanning. An iterative method based Fresnel diffraction imaging model is developed in this work, which directly reconstructs the refractive index decrement δ and imaginary β of the object from observed data ("one-step"). Moreover, high-quality material decomposition results are obtained by using a linear approximation in the iterative process. Compared with the existing methods, Our method shows a higher peak signal-to-noise ratio and structural similarity in numerical experimental results. Additionally, the quantitation accuracy of the proposed method is greater than 97.2 % by calculating the equivalent atomic number of the decomposed basic material in the real experiment. We demonstrate that this one-step method greatly reduces noise and improves quantitative reconstruction and decomposition results. This algorithm has the potential for quantitative imaging research using In-line tomography in future biomedical applications.

A Fast and Adaptive Empirical Mode Decomposition Method and Its Application in Rolling Bearing Fault Diagnosis

Although the ensemble empirical mode decomposition (EEMD) method and the complementary EEMD (CEEMD) method can greatly improve the mode mixing of the original empirical mode decomposition (EMD) method, the difference in white noise amplitude and the number of ensemble trials have a great influence on the decomposition results, and the low computational efficiency cannot meet the requirements of the online monitoring system. To solve these problems, a fast and adaptive EMD (FAEMD) method was proposed in this article, which combines the advantages of the order statistics filter (OSF) with the original EMD. First, the upper envelope and lower envelope of the original signal were drawn by using the OSF. Then, the original signal was decomposed into a series of intrinsic mode functions (IMFs) by the mean envelopes. Finally, the fault feature information was obtained by using the envelope spectrum analysis. The simulation signal and two groups of test fault signals were taken as examples to verify the effectiveness of the proposed method. Compared with EMD, CEEMD, and CEEMDAN methods, FAEMD can effectively extract the key feature information of fault signals and has strong practicability because of the low calculation cost.

Assessing consumption-based carbon footprint of China's food industry in global supply chain

With the acceleration of industrialization and rapid demand growth, carbon emissions increased dramatically in China's food industry during the past two decades. However, the uncertainty of the emission level of China's food industry alongside the global supply chain restricts China from implementing more sustainable food policies. Thus, this paper first estimates the carbon footprint in China's food manufacturing industry domestically and internationally by identifying major trade partners and the corresponding industries using a multi-regional input-output model. Then, this paper further decomposed the determinants of carbon footprint change. The results show that agriculture, food industry, transport, and energy supply are primarily responsible for carbon emission growth. The structural decomposition results further demonstrate that increasing demand and consumption pattern changes are important factors leading to a growth in carbon emission, and energy efficiency improvement helps China's food industry mitigate the emission level. Finally, policy recommendations are provided based on the decomposition results.

Spectral Attributes in Onshore Fuba Field Niger-Delta, Nigeria, Using 3-D Seismic Time-lapse Data

Spectral attributes in Onshore Fuba Field Niger Delta, Nigeria, are here presented, using 3D seismic time-lapse data. The FUBA Field lies on latitudes 40 50’58’’- 4 0 55’19’’N and longitudes 60 18’41’’- 6 0 26’41’’E with aerial extent of 840km2 . The base (1997) and the monitor (2009) seismic surveys resulted in a 4D response difference. The Base and Monitor data have a root-mean-square repeatability ratio (RRR) of 0.38 implying a very good repeatability when considering the acquisition, processing and environmental noises. Data processing and interpretation were carried out using Petrel software. Reservoir pressure decline rate of 0.062psi/day resulted in production decline rate of 1192.21bbl/day. Structural interpretation of seismic data reveals a highly faulted field. Two distinct horizons were mapped. Fault and horizon interpretation shows closures that are collapsed crestal structures bounded by two major faults. All the interpreted faults are normal synthetic and antithetic faults which are common in the Niger Delta basin. The depth structure maps reveal anticlinal faults. Reservoirs are found at a shallower depth from 6500 to 7500 ft and at a deeper depth ranging from 11500 to 13000 ft. The variance values range from 0.0 to 1.0. The Variance edge analysis was used to delineate the prominent and subtle faults in the area. The results of spectral decomposition at the different frequencies indicates areas of low frequency and high amplitude associated with known hydrocarbon zones, meandering channels, lobes and the presence of small scale faults in the field. The results of the work will help in the recovery of more hydrocarbon as by-passed zones and subtle structures are revealed in the area of study.

Функциональное представление процедур компрометации в процессе воздействия вредоносного программного обеспечения на информационные ресурсы компьютерной системы

This article is the second one in a series of articles devoted to the functional presentation of threats of virus attacks on computer systems’ information resources (CS). A functional model of the first and second stages of the implementation of such threats — the stages of compromising individual elements of the CS computer network and compromising the entire network as a whole is described. The results of functional decomposition to the level of functions implemented in the process of performing the procedures of these stages are presented. A mathematical model of the time characteristics of procedures is given.

Функциональное представление процедур подавления вредоносной активности в операционной среде компьютерной системы

This article is the final one in a series of articles devoted to the functional representation of anti-virus protection measures in computer systems. It describes the functional model of the second stage of the process of preventing threats from the impact of malware on computer systems information resources - the stage of suppressing malicious activity in the operating environment of these systems. The results of the functional decomposition of this stage to the level of functions implemented in the process of performing procedures for auditing the normal functioning of the operating environment for the manifestation of anomalies in its behavior and response to manifestations of the threat of exposure to malware are presented. Mathematical models of time characteristics of these procedures are given

Seismic Attributes for Enhanced Structural and Stratigraphic Interpretation in Onshore Fuba field, Niger Delta, Nigeria

Seismic attributes for enhanced structural and stratigraphic interpretation of Fuba Field, Onshore Niger Delta, Nigeria using Well-log and 3D Seismic data are here presented. Well-to-Seismic ties, faults and horizon mapping, time-surface generation, depth conversion and seismic attributes generation were carried out using Petrel software. The structural interpretation of seismic data reveal highly synthetic and antithetic faults which are in line with faults trends identified in the Niger Delta. Of the 29 interpreted faults, only synthetic and antithetic faults are regional, running from the top to bottom across the field. These faults play significant roles in trap formation at the upper, middle and lower sections of the field. Two distinct horizons were mapped. Fault and horizon interpretation reveal closures which are collapsed crestal structures bounded by these two major faults. The depth structure maps reveal anticlinal faults. Reservoirs are found at a shallower depth from 6500 to 7500ft and at a deeper depth ranging from 11500 to 13000ft. The synthetic and antithetic faults act as good traps for the hydrocarbon accumulation in the study area. The variance and chaos values range from 0.0 to 1.0. The dip magnitude and azimuth values range from 0 to 90 degrees and 0 to 360 degrees. The variance edge, chaos, dip magnitude and azimuth analysis were used to delineate the prominent and subtle faults in the area. The results of spectral decomposition at a frequency of 12-35Hz indicates areas of low frequency and high amplitude associated with known hydrocarbon zones, the presence of channels, lobes and small scale faults in the field. The result of the seismic attribute analysis has shown that the Fuba field has good hydrocarbon prospects.

Application of Time-Frequency Decomposition and Seismic Attributes for Stratigraphic Interpretation of Thin Reservoirs in Onshore Fuba Field Niger-Delta, Nigeria

Application of time-frequency decomposition and seismic attributes for stratigraphic interpretation of thin reservoirs in Onshore Fuba Field Niger Delta, Nigeria, are here presented, using Well-log and 3D Seismic data. Well-to-seismic tie, fault mapping, horizon mapping, time surface generation, depth conversion and seismic attributes generation were carried out using Petrel software. Structural interpretation of seismic data reveals a highly faulted field. Two distinct horizons were mapped. Fault and horizon interpretation shows closures that are collapsed crestal structures bounded by two major faults. All the interpreted faults are normal synthetic and antithetic faults which are common in the Niger Delta basin. The depth structure maps reveal anticlinal faults. Reservoirs are found at a shallower depth from 6500 to 7500 ft and at a deeper depth ranging from 11500 to 13000 ft. The variance and chaos values range from 0.0 to 1.0. The Variance edge and chaos analysis were used to delineate the prominent and subtle faults in the area while the produced spectra were used to delineate the stratigraphy and thickness of the thin-bedded reservoirs. The results of spectral decomposition at frequencies between 12Hz and 35Hz indicate some thin pay sand zones reservoirs which were characterized by of low frequency and high amplitude associated with known hydrocarbon zones, meandering channels, lobes and the presence of small scale faults in the field. Six new probable zones (Prospects A, B, C, D, E and F) of hydrocarbon accumulation were identified. The results of the study will help in the recovery of more hydrocarbon as by-passed zones and subtle structures are revealed in the area of study.

Функциональное представление процедур предупреждения вредоносной активности в компьютерной системе

This article is the fifth in a series of articles devoted to the application of the methodological apparatus of functional modeling for the study of problems in the field of computer virology. It describes the functional model of the first stage in the implementation of anti-virus protection measures in computer systems - the stage of preventing malicious activity. The results of functional decomposition to the level of functions implemented in the process of executing the procedures of this stage are presented. Mathematical models of time characteristics of procedures are given

Study on provincial decomposition method of total carbon emission control target based on greenhouse gas inventory

Realizing the transformation from “dual control of energy consumption” to “dual control of carbon emissions” is the inherent requirement of realizing the “dual carbon” goal. Scientific planning of the provincial decomposition method of total carbon emissions is an important content of the top-level design of carbon emissions management, and an important support for the implementation of total carbon emissions control. Based on the application research of greenhouse gas inventory in all fields, this paper conducts a full factor analysis of the total carbon emissions of the whole society, establishes a comprehensive decomposition index system, and proposes a simple and transparent provincial decomposition method of the total carbon emissions control target, which considers fairness and efficiency. The simulated decomposition results show that the decomposition method is scientific and operable and can be used as an important reference for future practical decomposition operations.

Wind Speed Prediction Model Based on Deep Learning

This article selects hourly wind speed data recorded by meteorological monitoring stations as the dataset, and conducts in-depth analysis on the preprocessing methods of wind speed data in response to the nonlinearity and instability of wind speed time series. At the same time, the algorithm principles and steps of empirical mode decomposition, comprehensive empirical mode decomposition, and complementary ensemble empirical mode decomposition were introduced, and the decomposition results of different methods were compared. In addition, in the selection of prediction algorithms for wind speed prediction models, the theoretical basis and algorithm steps of backpropagation neural networks, deep confidence networks, and long-term and short-term memory neural networks were studied, and a single model prediction performance comparison was conducted on three time series short-term prediction models. Compared with the LSTM model, the RMSE of the model established in this article decreased by 0.9422, MAE decreased by 0.6789, and MAPE decreased by 7.23%.

Effects of Uniaxial Bianisotropic Media on Full-Polarimetric GPR Signatures

The polarimetric response of full-polarimetric ground penetrating radar (FP-GPR) from anisotropic media is a pressing issue to be investigated. It can improve the detection accuracy of the targets in the anisotropic media and can also provide effective ways to detect anisotropic targets. In this paper, we focus on the FP-GPR signals from uniaxial bi-anisotropic media, i.e. the complete case that both the permittivity and the conductivity of the background media are anisotropic. Based on the propagation characteristics of electromagnetic waves in the anisotropic media and the transmission coefficients on the surface of the media, we construct a mathematical relation between the measured scattering matrix affected by anisotropies and the real scattering matrix, derive a coefficient to characterize the polarization rotation(PR) effects, and proposed a diagonal matrix for target decomposition analyses. The computation results indicate that ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>z</i></sub> /ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> , σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>z</i></sub> /σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> , and antenna interval have complex effects on the PR coefficient and the H-Alpha decomposition results of three types of typical targets. Multiple 3D FP-GPR simulations on a series of uniaxial bi-anisotropic models verified the results.

Analisis Utang Pemerintah, PMA, Ekspor, Inflasi dan Pertumbuhan Ekonomi dengan Pendekatan Panel VAR

This study aims to determine the causality relationship between variables, impulse response and variance decomposition of sources of development financing for a country in Asia from macroeconomic variables, namely government debt, foreign investment (FDI), exports, inflation and economic growth using the Panel VAR method and the operation of the STATA software. The impulse response results show that the shock of the government debt variable and inflation was responded positively by the economic growth variable. FDI and export variables responded negatively by the economic growth variable. The results of variance decomposition show that the export variable is a driving factor for economic growth in the Asian region.

An economy-wide rebound effect analysis of Kenya's energy efficiency initiatives

Adoption and improvement of energy efficiency is one of the recommended pathways to tackle the problems of growing energy demand and greenhouse gas emissions. However, studies show that rebound effects from energy efficiency improvements can erode anticipated energy savings. This paper elucidates the rebound effect arising from energy efficiency initiatives in Kenya. The study uses Index Decomposition Analysis to assess the drivers of Kenya's total primary energy supply. The paper then adopts the neoclassical growth model in the empirical analysis of energy and carbon rebound effects at the macro-economic level. Cobb-Douglas production function is further applied using Solow growth model and energy as a factor of production to approximate technological progress. Results of decomposition of energy supply show that energy intensity and affluence are the drivers with the most significant effect on energy supply while population has less critical effect. Average energy and carbon rebound effects show partial rebounds of 15.64% and 31.41%, respectively. These results imply that there is need to accelerate the pace of transitioning to green energy as the ultimate solution to minimizing environmental impacts arising from energy use and tackling climate change.

The effects of the oil price and temperature on food inflation in Latin America.

The impacts on food prices of temperature, the oil price, the exchange rate and wages in the agricultural industry were examined via a structural vector autoregression model and panel Granger causality test, using monthly data between January 2003 and December 2020 for Latin American countries. The paper concerns how much the determinants affect food prices. Empirical findings show that the oil price and temperature can be significant factors for reducing food inflation. According to the result of variance decomposition, in general, a considerable part of food inflation was explained by the exchange rate, but its effect did not show any significant change in the long term. The impacts of the oil price and temperature were limited in the early months, but they created larger changes over time. Impulse response function and the Granger causality test also indicated that exchange rate was a crucial dynamic in explaining food inflation in all countries except Ecuador. This country successfully mitigated the negative effect of the exchange rate, but the oil price and temperature had an impact on food inflation. All results indicate that both monetary and fiscal policies are essential to control food prices. These countries can accomplish this by conventional policies or by radical institutional changes. Nevertheless, the oil price and temperature are external dynamics, and crucial in creating alternative policies to control food inflation.

Obstructive Sleep Apnea Detection based on ECG Signal using Statistical Features of Wavelet Subband

One of the respiratory disorders is obstructive sleep apnea (OSA). OSA occurs when a person sleeps. OSA causes breathing to stop momentarily due to obstruction in the airways. In this condition, people with OSA will be deprived of oxygen, sleep awake and short of breath. Diagnosis of OSA by a doctor can be done by confirming the patient's complaints during sleep, sleep patterns, and other symptoms that point to OSA. Another way of diagnosing OSA is a polysomnography (PSG) examination in the laboratory to analyze apnea and hypopnea. However, this examination tends to be high cost and time consuming. An alternative diagnostic tool is an electrocardiogram (ECG) examination referring to changes in the mechanism of ECG-derived respiration (EDR). So digital ECG signal analysis is a potential tool for OSA detection. Therefore, in this study, it is proposed to classify OSA based on ECG signals using wavelets and statistical parameters. Statistical parameters include mean, variance, skewness kurtosis entropy calculated on the signal decomposition results. The validation performance of the proposed method is carried out using a support vector machine, k-nearest neighbor (k-NN), and ensemble classifier. The proposed method produces the highest accuracy of 89.2% using a bagged tree where all features are used as predictors. From this study, it is hoped that ECG signal analysis can be used to complete clinical diagnosis in detecting OSA.

Cyanate dynamics under algal blooms and sediment resuspension events in a shallow micro-tidal estuary in the lower Chesapeake Bay

Although an emerging component in the marine nitrogen (N) cycle, cyanate concentrations and cycling have not been examined in estuarine systems to date. To better understand controls on cyanate concentrations in estuaries, time series data of cyanate and nutrient concentrations in the Lafayette River, a micro-tidal, sub-tributary of the lower Chesapeake Bay, were examined between June and September 2018, and May to September 2019. Cyanate concentrations ranged from near the detection limit (0.4 nmol L−1) to 82.9 nmol L−1 in 2018, and 6.8–207.4 nmol L−1 in 2019. Variations in cyanate concentrations were highly correlated with chlorophyll biomass in the summer, biomass degradation in early fall, and sediment resuspension that occurred in response to meteorological forcing. Cyanate concentrations increased after Chl a concentrations decreased suggesting algal decomposition as a source of cyanate. High cyanate concentrations in bottom waters, corresponded to wind-induced sediment resuspension events in the Lafayette River, again suggesting organic matter decomposition as a source of cyanate. Cyanate concentrations in sediment pore water varied between years; in summer 2018, cyanate concentrations were up to 150 nmol L−1, while in 2019, they were three times lower. To confirm an algal source for cyanate, a degradation experiment was conducted using Lafayette River water collected during a bloom of Margalefidinium polykrikoides in 2018. In dark incubation bottles, cyanate was one of the first labile organic nitrogen products produced, suggesting the contention that high concentrations of cyanate in late summer and fall were the result of organic matter decomposition. Neither cyanate nor ammonium accumulated in light bottles suggesting production and uptake are tightly coupled and microbes have a high affinity for cyanate in the light. In dark bottles, cyanate production rates were 6.8 nmol L−1 d−1, while microbial removal rates during the late phase of degradation were 1.5 nmol L−1 d−1, suggesting that cyanate may not be a preferred nitrogen substrate for microbes (including nitrifiers) in dark bottles or that microbes have a lower affinity for cyanate in the dark, allowing cyanate to reach steady state at concentrations greater than the detection limit.

Decomposition of socioeconomic inequalities in catastrophic out-of-pocket expenditure for healthcare in Canada

Despite a publicly financed health system for physician and hospital services, out-of-pocket health expenditure (OHE) accounts for a significant proportion of healthcare financing in Canada. We pooled annual Surveys of Household Spending conducted from 2010 to 2017 (n=34,105) to estimate the catastrophic out-of-pocket expenditure (COHE) burden using two definitions: the budget share (OHE exceeding 10% of a household's total consumption) and capacity-to-pay (OHE exceeding 40% of a household's total consumption minus basic subsistence needs). The Wagstaff index (WI) and the Erreygers Index (EI) were used to quantify and decompose socioeconomic inequalities in COHE. Results demonstrate that approximately 6% and 10% of the households faced COHE in Canada, depending on whether we used the budget share or capacity-to-pay approach to measure COHE. The COHE was found to be concentrated among low socioeconomic status (SES) households. Decomposition results indicate that besides SES, household characteristics (e.g., households headed by females and the presence of senior(s) in the households) were the most important factors contributing to the concentration of COHE among the poorer households. The lower utilization of healthcare services among the poor resulted in reduced COHE among these households. A higher burden of COHE is a major concern in Canada. Policies to enhance risk protection among specific populations such as the seniors are required to improve equity in healthcare financing in Canada.

China’s power industry’s carbon emission intensity in the context of carbon peaking and carbon neutrality: measurement and regional difference

PurposeTo cope with the severe situation of the global climate, China proposed the “30 60” dual-carbon strategic goal. Based on this background, the purpose of this paper is to investigate scientifically and reasonably the interprovincial pattern of China’s power carbon emission intensity and further explore the causes of differences on this basis.Design/methodology/approachConsidering the principle of “shared but differentiated responsibilities,” this study measures the carbon emissions within the power industry from 1997 to 2019 scientifically, via the panel data of 30 provinces in China. The power carbon emission intensity is chosen as the indicator. Using the Dagum Gini coefficient to explore regional differences and their causes.FindingsThe results of this paper show that, first, China’s carbon emission intensity from the power industry overall is significantly different. From the perspective of geospatial distribution, the three regions have unbalanced characteristics. Second, according to the decomposition results of the Gini coefficient, the overall difference in power carbon emission intensity is generally expanding. The geospatial and economic development levels are examined separately. The gaps between the eastern and economically developed regions are the smallest, and the regional differences are the source of the overall disparity.Research limitations/implicationsFurther exploring the causes of differences on this basis is crucial for relevant departments to formulate differentiated energy conservation and emission reduction policies. This study provides direction for analyzing the green and low carbon development of China’s power industry.Practical implicationsAs an economic indicator of green and low-carbon development, CO2 intensity of power industry can directly reflect the dependence of economic growth on the high emission of electricity and energy. and further exploring the causes of differences on this basis is crucial for relevant departments to formulate differentiated energy conservation and emission reduction policies.Social implicationsFor a long time, with the rapid economic development, resulting in the unresolved contradiction between low energy efficiency and high carbon emissions. To this end, scientifically and reasonably investigating the interprovincial pattern of China’s power carbon emission intensity, and further exploring the causes of differences on this basis, is crucial for relevant departments to formulate differentiated energy conservation and emission reduction policies.Originality/valueThird, considering the influence of spatial factors on the convergence of power carbon emission intensity, a variety of different spatial weight matrices are selected. Based on the β-convergence theory from both absolute and conditional perspectives, we dig deeper into the spatial convergence of electricity carbon emission intensity across the country and the three regions.