Decomposition Coefficients Research Articles

High-temperature mid-IR absorption and reaction kinetics of 2-methyl-1,3-dioxolane: An experimental and theoretical study

This work reports the mid-IR spectroscopy and reaction kinetics of 2-methyl-1,3-dioxolane (2M13DO). We carried out spectroscopic measurements to deduce temperature-dependent absorption cross-sections of 2M13DO over a broad wavelength range of 8.4–10.5 μm (950–1190 cm−1). For these measurements, we employed a rapidly tuning MIRcat-QT™ laser that can be operated either at a fixed wavelength or scanned mode over wide wavelength regions. By operating the laser at a fixed wavelength, we monitored the decay of 2M13DO behind reflected shock waves over T5 = 1050–1400 K and P5 = 0.7 and 2.6 bar. Our measured concentration time-histories of 2M13DO allowed us to directly extract the overall rate coefficients for the unimolecular decomposition of 2M13DO using the first-order rate law. We did not observe any pressure dependence in the measured rate coefficients, indicating that the reaction is close to the high-pressure limit. By employing the W1U composite method, we explored the important pyrolysis reaction pathways of 2M13DO in the reactive potential energy surface. Three important reaction channels, namely, 2M13DO → CH2CHOCH2CH2OH (IM1), 2M13DO → 2CH3CHO (P3), 2M13DO → CH3 + 1,3-dioxolan-2-yl (P4) were identified. Below 700 K, IM1 forming channel is dominant, whereas CH3CHO formation is dominant under our experimental conditions. Above 1500 K, the radical forming channel (CH3+P4) takes over other channels. At higher temperatures, the contribution of the radical forming channel continually increases, accounting for ∼ 99% at 2000 K. We used the stochastic RRKM-ME model to predict the pressure and temperature dependence of the rate coefficients, k(T, P), and time-resolved species profiles. Our theory showed excellent agreement with the measured rate coefficients. These are the first direct determination of the rate coefficients of the unimolecular decomposition of 2M13DO.

Hilbert transform of unequally sampled data: Application to dispersion relations in magnetotellurics

The real and imaginary parts of the Fourier transform of a causal signal can be estimated from one another using the Hilbert transform. The numerical computation can be carried out by a fast Fourier transform or the convolution of the data with an appropriate Hilbert kernel. However, magnetotelluric (MT) data are unequally sampled because logarithmic frequency axes are conventionally used. A method has been developed for the Hilbert transformation of unequally sampled data and applied to the real and imaginary parts of the frequency normalized impedance to examine if the dispersion relations are satisfied for a given impedance tensor. A version of the Shannon sampling theory is generalized for the unequally sampled data. The method involves the reconstruction of the measured data by a linear combination of an analytic interpolation function distributed along the horizontal axis. Then, the decomposition coefficients that provide a fit between the measured and reconstructed data are solved using the linear least-squares method with singular-value decomposition. Finally, the discrete Hilbert transformed data are constructed at any desired abscissa values by the sum of the analytic Hilbert transform of the interpolation function using the decomposition coefficients solved in the previous step. Examples from the MT synthetic and field data, including out-of-quadrant phase cases, are given. In principle, the method also can be applied to other integral transforms.

Low-cost mineral packing materials improve DOM and micropollutants removal from landfill leachate in ozonation bubble columns: Insights into the enhancement mechanisms and applicability of surrogate-based monitoring

To intensify O3 mass transfer and increase removal of micropollutants in bio-treated landfill leachate, this study applied two minerals (lava rock and expanded clay aggregate (ECA)) to build mineral-packed bubble columns (named as LBC and EBC, respectively). The O3 gas–liquid mass transfer coefficient kLa of EBC (0.58 min−1) and LBC (0.62 min−1) was 2.4–2.5 times that of a non-packed bubble column (BC, 0.24 min−1). The O3 decomposition coefficient kd was also increased to 0.066 min−1 in EBC and 0.050 min−1 in LBC, as ECA and lava rock could work as stable catalysts to promote the O3 decomposition and generation of •OH. LBC and EBC had a comparable beneficial effect towards micropollutants removal and their intermediates’ degradation. At O3 dose of 0.08–1.46 g O3/g COD, minerals increased the removal of O3-recalcitrant compounds (atrazine and alachlor) by 20–40 % and the UV254 reduction (ΔUV254) by over 20 %. The enhancement of LBC and EBC resulted from the improved O3 mass transfer and the catalytic function of the minerals. Besides, a two-stage linear correlation between the O3-recalcitrant compounds removal and ΔUV254was observed. The first and second stage are dominated by the direct O3 reaction and •OH oxidation process, respectively. For the packed columns, removal mainly occurred at the second stage probably due to the improved •OH production by the packing materials. Based on such correlation model, ΔUV254 can be used as a surrogate for predicting the removal of micropollutants in packed columns. Overall, this study provides novel insights into the enhanced micropollutants abatement in landfill leachate by using mineral-packed ozone bubble columns.

Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences

Decision support systems can seriously help medical doctors in the diagnosis of different diseases, especially in complicated cases. This article is devoted to recognizing and diagnosing heart disease based on automatic computer processing of the electrocardiograms (ECG) of patients. In the general case, the change of the ECG parameters can be presented as a random sequence of the signals under processing. Developing new computational methods for such signal processing is an important research problem in creating efficient medical decision support systems. Authors consider the possibility of increasing the diagnostic accuracy of cardiovascular diseases by implementing of the new proposed computational method of information processing. This method is based on the generalized nonlinear canonical decomposition of a random sequence of the change of cardiogram parameters. The use of a nonlinear canonical model makes it possible to significantly simplify the maximum likelihood criterion for classifying diseases. This simplification is provided by the transition from a multi-dimensional distribution density of cardiogram parameters to a product of one-dimensional distribution densities of independent random coefficients of a nonlinear canonical decomposition. The absence of any restrictions on the class of random sequences under study makes it possible to achieve maximum accuracy in diagnosing cardiovascular diseases. Functional diagrams for implementing the proposed method reflecting the features of its application are presented. The quantitative parameters of the core of the computational diagnostic procedure can be determined in advance based on the preliminary statistical data of the ECGs for different heart diseases. That is why the developed method is quite simple in terms of computation (computing complexity, accuracy, computing time, etc.) and can be implemented in medical computer decision systems for monitoring cardiovascular diseases and for their diagnosis in real time. The results of the numerical experiment confirm the high accuracy of the developed method for classifying cardiovascular diseases.

Pathological voice classification based on multi-domain features and deep hierarchical extreme learning machine

The intelligent data-driven screening of pathological voice signals is a non-invasive and real-time tool for computer-aided diagnosis that has attracted increasing attention from researchers and clinicians. In this paper, the authors propose multi-domain features and the hierarchical extreme learning machine (H-ELM) for the automatic identification of voice disorders. A sufficient number of sensitive features are first extracted from the original voice signal through multi-domain feature extraction (i.e., features of the time domain and the sample entropy based on ensemble empirical mode decomposition and gammatone frequency cepstral coefficients). To eliminate redundancy in high-dimensional features, neighborhood component analysis is then applied to filter out sensitive features from the high-dimensional feature vectors to improve the efficiency of network training and reduce overfitting. The sensitive features thus obtained are then used to train the H-ELM for pathological voice classification. The results of the experiments showed that the sensitivity, specificity, F1 score, and accuracy of the H-ELM were 99.37%, 98.61%, 99.37%, and 98.99%, respectively. Therefore, the proposed method is feasible for the initial classification of pathological voice signals.

Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environment

A deep learning strategy is developed for fast and accurate gas property measurements using flame emission spectroscopy (FES). Particularly, the short-gated fast FES is essential to resolve fast-evolving combustion behaviors. However, as the exposure time for capturing the flame emission spectrum gets shorter, the signal-to-noise ratio (SNR) decreases, and characteristic spectral features indicating the gas properties become relatively weaker. Then, the property estimation based on the short-gated spectrum is difficult and inaccurate. Denoising convolutional neural networks (CNN) can enhance the SNR of the short-gated spectrum. A new CNN architecture including a reversible down- and up-sampling (DU) operator and a loss function based on proper orthogonal decomposition (POD) coefficients is proposed. For training and testing the CNN, flame chemiluminescence spectra were captured from a stable methane-air flat flame using a portable spectrometer (spectral range: 250–850 nm, resolution: 0.5 nm) with varied equivalence ratio (0.8–1.2), pressure (1–10 bar), and exposure time (0.05, 0.2, 0.4, and 2 s). The long exposure (2 s) spectra were used as the ground truth when training the denoising CNN. A kriging model with POD is trained by the long-gated spectra for calibration, and then the prediction of the gas properties taking the denoised short-gated spectrum as the input: The property prediction errors of pressure and equivalence ratio were remarkably lowered in spite of the low SNR attendant with reduced exposure.

Remote Fault Identification and Analysis in Electrical Distribution Network Using Artificial Intelligence

This research describes a method for wavelet decomposition and machine learning-based fault site classification in a radial power distribution network. The first statistical observation is produced using wavelet decomposition and wavelet-based detailed coefficients in terms of Kurtosis and Skewness parameters. For this objective, six distinct machine learning methods are deployed. They are evaluated and compared using unknown data sets with varying degrees of unpredictability. One approach has been shown to be the most accurate in locating the location of the problem bus.

ОПТИМІЗАЦІЙНИЙ МЕТОД КОМРЕСІЇ БАГАТОСПЕКТРАЛЬНИХ ЦИФРОВИХ ЗОБРАЖЕНЬ ПРОЕКЦІЙНОЇ ПРИРОДИ

A method for compressing digital images of a projection nature obtained in an arbitrary amount of spectral radiation ranges - a carrier of specific information or under various positional conditions of formation, optimized according to the criteria for preserving the specified levels of signal image energy and their structural similarity, is proposed. The method uses the representation of a set of such images in the form of a single multidimensional geometric object, which is described by a two-dimensional data array ordered by raster and spectral intervals and includes preprocessing by means of singular decomposition of the specified array. Actual compression is carried out by means of decomposition the constituent images, represented by maximum singular numbers, according to discrete orthonormed functional bases, zeroing out part of the decomposition coefficients and subsequent reconstruction of the digital brightness codes of images. The determination of the thresholds for zeroing the decomposition coefficients of images digital levels is formulated in the form of a two-criteria optimization problem of obtaining the given ratios of signal energies of the compressed and initial images and indices of their structural similarity. The implementation of the proposed methods includes the following steps: pairwise orthogonalization of distributions of digital brightness codes of images in different spectral channels based on singular decompositions; preservation of the constituent distributions of brightness with maximum singular numbers; compression of orthogonalized representations according to the specified criteria; reconstructing the brightness codes of compressed images of spectral channels by a functional transformation reversed by the one used at the stage of decomposition of brightness distributions of directly recorded multispectral images. Comparison of various discretized functional bases as the basis for compression showed the greatest efficiency in the specified criteria of the discrete functional basis of Haar. Key words: multispectral image, orthogonalization, signal energy, structural similarity index, orthonormed basis.

ELECTROMAGNETIC COUPLING OF PIEZOELECTRIC/FERRITE COMPOSITE WITH INITIAL STRESS STATE

A mathematical model of electromagnetic thermoelasticity for an initial-stressed transversal-isotropic composite with piezoelectric magnetostrictive phases has been developed. To solve the related boundary value problem of electromagnetic thermoe-lasticity, the Green function method was used as part of a generalized singular approximation of the statistical mechanics of composites, taking into account the initial stress state of the representative domain of the composite at micro- and macro-levels. The solution of the problem of "effective module" for tensors of effective elastic, piezomechanical, magnetostrictive properties, dielectric permittivity and magnetic permit-tivity, temperature, pyroelectric, pyromagnetic coefficients and (which appeared only at the macro level) electromagnetic and magnetoelectric couplings of a quasi-periodic composite with an initial electromagnetic elastic state was obtained. The solutions for the desired tensors of effective properties of the quasi-periodic composite are presented in the form of analytical formulas of simple linear decompositions by solutions for tensors of effective properties of the periodic structure and statistical mixture, decomposition coefficients are the coefficient of "periodicity" (correlation of quasi-periodic and periodic structures) p and "disordering" 1-p, respectively. Results of calculation of all independent components of tensors of effective coefficients of electromagnetic and magnetoelectric couplings of different structures (periodic, quasi-periodic and statistical mixture) are presented in unidirectional direction of fibrous composite "PZT-4/ferrite" with axisymmet-ric tensor of initial macrostrain of composite. For a quasi-periodic composite (with initial macrostrain), the significantly non-monotonic nature of the dependencies of relative (to values in the absence of an initial stress state) values of effective coefficients of electromagnetic and magnetoelectric couplings from the volume fraction of ferrite fibers was revealed. It was revealed that we have an increase in the absolute values of the electromagnetic and magnetoelectric coupling coefficients of composite at negative values of its initial axisymmetric axial macrostrains. The most significant effect is the initial comprehensive macrostrain in the transversal plane on the coefficients of the transversal magnetoelectric coupling of the composite.

Mechanism of front stagnation point unsteadiness for flow around a square cylinder

The unsteady characteristics and mechanisms of the front stagnation point oscillation for the flow around a square cylinder are investigated with the two-dimensional time-resolved particle image velocimetry technique in the range of 1420 ≤ Re ≤ 8600. The front stagnation point oscillates with the frequency equal to the wake vortex-shedding frequency. The Reynolds number has no effect on the amplitude and the dimensionless frequency of the front stagnation point. It is revealed that the front stagnation point moves from the centerline to one side when the wake vortex from the opposite side is forming and moving toward the centerline. This growing wake vortex stays attached to the rear surface of the square cylinder. After the wake vortex is detached from the rear surface, the front stagnation moves from the maximum displacement back toward the centerline. Furthermore, the quantitative correspondence between the location of front stagnation point and the wake pattern is revealed via time-lag correlation. The movement of the front stagnation point and the variation of typical proper orthogonal decomposition coefficient of the wake are nearly opposite in phase.

Robust Watermarking Scheme for Vector Geographic Data Based on the Ratio Invariance of DWT–CSVD Coefficients

Traditional frequency-domain watermarking algorithms for vector geographic data suffer from disadvantages such as the random watermark embedding position, unpredictable embedding strength, and difficulty in resisting multiple attacks at the same time. To address these problems, we propose a novel watermarking algorithm based on the geometric invariance of the ratios of discrete wavelet transform (DWT) and complex singular value decomposition (CSVD) coefficients, which embeds the watermark information in a new embedding domain. The proposed scheme first extracts feature points from the original vector geographic data using the Douglas–Peucker algorithm, and then constructs a complex sequence based on the feature points set. The two-level DWT is then performed on the complex sequence to obtain the low frequency coefficients (L2) and high frequency coefficients (H2). On this premise, the CSVD algorithm is utilized to calculate the singular values of L2 and H2, and the ratio of the singular values of L2 and H2 is acquired as the watermark embedding domain. During the watermark embedding process, a new watermark sequence is created by the fusion of the original watermark index value and bits value to improve the recognition of the watermark information, and the decimal part at different positions of the ratio is altered by the new watermark sequence to control the watermark embedding strength. The experimental results show that the proposed watermarking algorithm is not only robust to common attacks such as geometric, cropping, simplification, and coordinate point editing, but also can extract watermark images with a high probability under random multiple attacks.

Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features.

In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification accuracy. Generally, the classical feature extraction techniques are sensitive to the noisy component of the signal and need more time for training. To deal with these issues, a comparatively new feature extraction technique, referred to as a wavelet scattering transform (WST) is utilized, and incorporated with ML classifiers to design a framework for bearing fault classification in this paper. The WST is a knowledge-based technique, and the structure is similar to the convolution neural network. This technique provides low-variance features of real-valued signals, which are usually necessary for classification tasks. These signals are resistant to signal deformation and preserve information at high frequencies. The current signal data from a publicly available dataset for three different bearing conditions are considered. By combining the scattering path coefficients, the decomposition coefficients from the 0th and 1st layers are considered as features. The experimental results demonstrate that WST-based features, when used with ensemble ML algorithms, could achieve more than 99% classification accuracy. The performance of ANN models with these features is similar. This work exhibits that utilizing WST coefficients for the motor current signal as features can improve the bearing fault classification accuracy when compared to other feature extraction approaches such as empirical wavelet transform (EWT), information fusion (IF), and wavelet packet decomposition (WPD). Thus, our proposed approach can be considered as an effective classification method for the fault diagnosis of rotating machinery.

Research on feature extraction and separation of mechanical multiple faults based on adaptive variational mode decomposition and comprehensive impact coefficient

Because it is difficult to extract multiple fault features from mechanical equipment under the interference of background noise and the parameters used in variational mode decomposition (VMD) must be determined in advance, a multiple fault separation method based on adaptive variational mode decomposition (AVMD) is proposed in this research to address these issues. Firstly, a novel index, known as the comprehensive impact coefficient (CIC), is established to properly identify the signal’s fault features. Thereafter, the fitness function of the sparrow search algorithm is developed based on the CIC, and the VMD parameters selection problem is solved. Finally, the decomposed modal components are subjected to envelop demodulation analysis, and the failure type of the bearing is assessed through the envelope spectrum. The simulation and experimental results reveal that the AVMD method can effectively separate all single faults from multiple faults, thus accurately diagnosing bearing faults.

Multiscale and local engineering of speckle morphology through disordered media.

In this Letter, we prompt a novel, to the best of our knowledge, method based on transmission matrix decomposition with wavelets to engineer the speckle morphology behind disordered media. By analyzing the speckles in multiscale spaces, we experimentally realized multiscale and localized control on the speckle size, position-dependent spatial frequency, and global morphology by operating on the decomposition coefficients using different masks. Speckles with contrasting features in different parts of the fields can be generated in one step. Our experimental results demonstrate a high degree of flexibility in manipulating light in a customizable manner. This technique has stimulating prospects in correlation control and imaging under scattering conditions.

A novel integrated method for short-term wind power forecasting based on fluctuation clustering and history matching

The intermittent and randomness of wind power poses a great challenge to the safe and stable operation of power systems. Accurate wind power forecasting is a key to reduce the negative impact of uncertainty on the entire system. To this end, a novel integrated method for short-term wind power forecasting is proposed based on frequency analysis, fluctuation clustering and history matching. Through variational modal decomposition (VMD) and Pearson correlation coefficient between wind resource and wind power, the wind power sequence is decomposed into a trend component and a fluctuation component. Then both the two components are sequentially divided into time series segments with equal lengths, which are set as the variation period determined by fast Fourier transform (FFT). Time series segments of the trend component are clustered by feature extraction and fuzzy C-means (FCM) clustering method, and accordingly the segments of fluctuation component are clustered as well. The correlation consistency between trend components and fluctuation components is further studied. Finally, the trend component is forecasted by random forest (RF) model, and the fluctuation component is forecasted by matching its counterpart trend component from historical similar segments database based on the correlation consistency. The performance and effectiveness of the proposed method are verified by using four wind farms in Jilin Province, China. Results indicate that the forecasting accuracy of the proposed method is greatly improved compared with the conventional forecasting model, which proves the validity.

Denoising method based on VMD-PCC in φ-OTDR system

To improve the signal-to-noise ratio (SNR) of vibration signal in phase-sensitive optical time domain reflectometry (φ-OTDR) system, a vibration signal denoising method based on variational mode decomposition (VMD) and Pearson correlation coefficient (PCC) is proposed. The phase signal demodulated by I/Q demodulation is further processed by a denoising method consisting of K layers VMD, where K was determined by calculating the ratio of adjacent intrinsic mode functions (IMFs) center frequencies, the Pearson correlation coefficient (PCC) estimation between each component and phase signal, and distortion and noise casting by discarding the IMF components with the PCC less than a threshold of 0.4 and the residual component of decomposition. The denoising method was verified in a 2 km φ-OTDR system in the scenarios of single 200 Hz frequency vibration at one position, two single frequencies of 200 Hz and 500 Hz vibration at two positions with minimum separation distance defined by the pulse width, and a multi-frequency vibration of 200 Hz, 1200 Hz and 2500 Hz at one position. And the demodulated phase signal by I/Q demodulation was processed by the proposed method, and further compared with the wavelet decomposition, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD). Experimental results show that the VMD-PCC method is superior to the other methods compared in improving the SNR of vibration signals and has excellent adaptability and effectiveness in φ-OTDR system.

High-temperature mid-IR absorption spectra and reaction kinetics of 1,3-dioxolane

Cyclic saturated ethers are proposed as alternative fuels for future sustainability. In particular, dioxolanes have been identified as promising biofuel candidates. However, pyrolysis and oxidation of dioxolanes are not well understood. In this work, we measured the temperature-dependent absorption cross-section of 1,3-dioxolane (13DO) in the mid-IR region over 8.4 – 10.5 μm (950 –1190 cm−1). We applied the spectroscopic knowledge to investigate the temperature and pressure dependence of the rate coefficients of the unimolecular decomposition of 13DO over the temperature range of 1000–1400 K and pressures near 0.7 and 2.4 bar. We employed a rapidly tuning MIRcat-QT™ laser in conjugation with a shock tube to carry out high-temperature spectroscopic and pyrolysis measurements. Measured IR spectra of 13DO exhibited a strong temperature dependence. For the kinetic study, we employed the vibrational feature of 13DO near 1120.5 cm−1 to monitor the decay of 13DO quantitatively. The measured rate coefficients did not show any discernible pressure dependence. This indicates that the rate coefficients of the unimolecular decomposition of 13DO are close to the high-pressure limit under the present experimental conditions. In addition, we rationalized the temperature and pressure dependence of the reaction by employing electronic structure/RRKM-ME calculations. Our theoretical model accurately captured the experimental trends of the rate coefficients of the unimolecular decomposition of 13DO.

Formation of Organic Acids and Carbonyl Compounds in n-Butane Oxidation via γ-Ketohydroperoxide Decomposition.

A crucial chain-branching step in autoignition is the decomposition of ketohydroperoxides (KHP) to form an oxy radical and OH. Other pathways compete with chain-branching, such as "Korcek" dissociation of γ-KHP to a carbonyl and an acid. Here we characterize the formation of a γ-KHP and its decomposition to formic acid+acetone products from observations of n-butane oxidation in two complementary experiments. In jet-stirred reactor measurements, KHP is observed above 590 K. The KHP concentration decreases with increasing temperature, whereas formic acid and acetone products increase. Observation of characteristic isotopologs acetone-d3 and formic acid-d0 in the oxidation of CH3 CD2 CD2 CH3 is consistent with a Korcek mechanism. In laser-initiated oxidation experiments of n-butane, formic acid and acetone are produced on the timescale of KHP removal. Modelling the time-resolved production of formic acid provides an estimated upper limit of 2 s-1 for the rate coefficient of KHP decomposition to formic acid+acetone.

Formation of Organic Acids and Carbonyl Compounds in n‐Butane Oxidation via γ‐Ketohydroperoxide Decomposition

AbstractA crucial chain‐branching step in autoignition is the decomposition of ketohydroperoxides (KHP) to form an oxy radical and OH. Other pathways compete with chain‐branching, such as “Korcek” dissociation of γ‐KHP to a carbonyl and an acid. Here we characterize the formation of a γ‐KHP and its decomposition to formic acid+acetone products from observations of n‐butane oxidation in two complementary experiments. In jet‐stirred reactor measurements, KHP is observed above 590 K. The KHP concentration decreases with increasing temperature, whereas formic acid and acetone products increase. Observation of characteristic isotopologs acetone‐d3 and formic acid‐d0 in the oxidation of CH3CD2CD2CH3 is consistent with a Korcek mechanism. In laser‐initiated oxidation experiments of n‐butane, formic acid and acetone are produced on the timescale of KHP removal. Modelling the time‐resolved production of formic acid provides an estimated upper limit of 2 s−1 for the rate coefficient of KHP decomposition to formic acid+acetone.

Differences in nitrogen and phosphorus sinks between the harvest and non-harvest of Miscanthus lutarioriparius in the Dongting Lake wetlands.

Plant non-harvest changes element circulation and has a marked effect on element sinks in the ecosystem. In this study, a field investigation was conducted on the fixation of nitrogen and phosphorus in Miscanthus lutarioriparius, the most dominant plant species in the Dongting Lake wetlands. Further, to quantitatively compare the difference in nitrogen and phosphorus sinks between harvest and non-harvest, an in situ experiment on the release of the two elements from two types of litters (leaves and stems) was studied. The nitrogen concentrations in the plant had no significant relationship with the environmental parameters. The phosphorus concentrations were positively related to the plot elevation, soil organic matter, and soil total potassium and were negatively related to the soil moisture. The leaves demonstrated a higher decomposition coefficient than that of the stems in the in situ experiment. The half decomposition time was 0.61 years for leaves and 1.12 years for stems, and the complete decomposition time was 2.83 years for leaves and 4.95 years for stems. Except for the nitrogen concentration in the leaves, all the concentrations increased during the flood period. All concentrations unsteadily changed in the backwater period. Similarly, except for the relative release index of nitrogen in the leaves, all the relative release indices decreased in the flood period. At the end of the in situ decomposition experiment, the relative release indices of both the nitrogen and phosphors were greater than zero, indicating that there was a net release of nitrogen and phosphorus. Under the harvest scenario, the aboveground parts of the plant were harvested and moved from the wetlands, thus increasing the nitrogen and phosphorus sinks linearly over time. The fixed nitrogen and phosphorus in the aboveground parts were released under the non-harvest scenario, gradually accumulating the nitrogen and phosphorus sinks from the first year to the fifth year after non-harvest, reaching a maximum value after the fifth year. This study showed that the nitrogen and phosphorus sinks greatly decreased after the non-harvest of M. lutarioriparius compared to that after harvest. It is recommended to continue harvesting the plant for enhancing the capacity of element sinks.