Potential Of Decomposition Research Articles

Assessing the potential of polarimetric decomposition of Sentinel-1 SAR for the estimation of mangrove forest biomass

Assessing the potential of polarimetric decomposition of Sentinel-1 SAR for the estimation of mangrove forest biomass

Dynamic mode decomposition of GRACE satellite data

Advancements in satellite technology yield environmental data with ever improving spatial coverage and temporal resolution. This necessitates the development of techniques to discern actionable information from large amounts of such data. We explore the potential of dynamic mode decomposition (DMD) to discover the dynamics of spatially correlated structures present in global-scale data, specifically in observations of total water storage anomalies provided by GRACE satellite missions. Our results demonstrate that DMD enables data compression and extrapolation from a reduced set of dominant spatiotemporal structures. The accuracy of its predictions of global system dynamics is preserved in its reconstruction of local time series. These findings suggest potential uses of DMD in analysis of remote-sensing data for hydrologic applications.

Separation of ventilation and perfusion of electrical impedance tomography image streams using multi-dimensional ensemble empirical mode decomposition

Objective. In the future, thoracic electrical impedance tomography (EIT) monitoring may include continuous and simultaneous tracking of both breathing and heart activity. However, an effective way to decompose an EIT image stream into physiological processes as ventilation-related and cardiac-related signals is missing.Approach. This study analyses the potential of Multi-dimensional Ensemble Empirical Mode Decomposition by application of the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and a novel frequency-based combination criterion for detrending, denoising and source separation of EIT image streams, collected from nine healthy male test subjects with similar age and constitution.Main results. In this paper, a novel approach to estimate the lung, the heart and the perfused regions of an EIT image is proposed, which is based on the Root Mean Square Error between the index of maximal respiratory and cardiac variation to their surroundings. The summation of the indexes of the respective regions reveals physiologically meaningful time signals, separated into the physiological bandwidths of ventilation and heart activity at rest. Moreover, the respective regions were compared with the relative thorax movement and photoplethysmogram (PPG) signal. In linear regression analysis and in the Bland–Altman plot, the beat-to-beat time course of both the ventilation-related signal and the cardiac-related signal showed a high similarity with the respective reference signal.Significance. Analysis of the data reveals a fair separation of ventilatory and cardiac activity realizing the aimed source separation, with optional detrending and denoising. For all performed analyses, a feasible correlation of 0.587 to 0.905 was found between the cardiac-related signal and the PPG signal.

Effect of in contents on the electronic and optical properties of 2D GaN/InxGa1-xN heterostructures

Two-dimensional gallium nitride (2D GaN) has recently received considerable attention due to its excellent physical properties, including high stability, wide and tunable bandgap, and magnetism. 2D GaN offers an accessible way to apply in photocatalytic hydrogen production as composing to vdW heterostructures. Here, we report a first principles study based on the HSE06 functional for the 2D van der Waals (vdW) GaN/InxGa1-xN heterostructures with a tunable bandgap achieved through modulation of indium (In) contents. Such heterostructure is a bilayer vertically stacked by a monolayer of GaN and a monolayer of InxGa1-xN, both of which are graphite-like structures. In particular, the heterostructure bandgap decreases from 3.37 eV to 2.23 eV as In content increases from 0 to 0.5, accompanying by a significant transition from indirect to direct band gap. All of the studied heterostructures have suitable band edge positions for the redox potential of water decomposition at pH 0. Furthermore, increasing In content improves the visible light absorption, carrier mobility and establishes a more effective built-in electric field in the heterostructures. Our results prove that 2D GaN/InxGa1-xN heterostructure is highly suitable as a catalyst for photocatalytic water splitting and provides theoretical guidance for applying 2D GaN in photocatalytic hydrogen production.

Lattice Engineering on Li2CO3-Based Sacrificial Cathode Prelithiation Agent for Improving the Energy Density of Li-Ion Battery Full-Cell.

Developing sacrificial cathode prelithiation technology to compensate for active lithium loss is vital for improving the energy density of lithium-ion battery full-cells. Li2CO3 owns high theoretical specific capacity, superior air stability, but poor conductivity as an insulator, acting as a promising but challenging prelithiation agent candidate. Herein, extracting a trace amount of Co from LiCoO2 (LCO), a lattice engineering is developed through substituting Li sites with Co and inducing Li defects to obtain a composite structure consisting of (Li0.906Co0.043▫0.051)2CO2.934 and ball milled LiCoO2 (Co-Li2CO3@LCO). Notably,both the bandgap and Li─O bond strength have essentially declined in this structure. Benefiting from the synergistic effect of Li defects and bulk phase catalytic regulation of Co, the potential of Li2CO3 deep decomposition significantly decreases from typical >4.7 to ≈4.25V versus Li/Li+, presenting >600 mAh g-1 compensation capacity. Impressively, coupling 5 wt% Co-Li2CO3@LCO within NCM-811 cathode, 235Wh kg-1 pouch-type full-cell is achieved, performing 88% capacity retention after 1000 cycles.

A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods

Air quality issues are critical to daily life and public health. However, air quality data are characterized by complexity and nonlinearity due to multiple factors. Coupled with the exponentially growing data volume, this provides both opportunities and challenges for utilizing deep learning techniques to reveal complex relationships in massive knowledge from multiple sources for correct air quality prediction. This paper proposes a prediction hybrid framework for air quality integrated with W-BiLSTM(PSO)-GRU and XGBoost methods. Exploiting the potential of wavelet decomposition and PSO parameter optimization, the prediction accuracy, stability and robustness was improved. The results indicate that the R2 values of PM2.5, PM10, SO2, CO, NO2, and O3 predictions exceeded 0.94, and the MAE and RMSE values were lower than 0.02 and 0.03, respectively. By integrating the state-of-the-art XGBoost algorithm, meteorological data from neighboring monitoring stations were taken into account to predict air quality trends, resulting in a wider range of forecasts. This strategic merger not only enhanced the prediction accuracy, but also effectively solved the problem of sudden interruption of monitoring. Rigorous analysis and careful experiments showed that the proposed method is effective and has high application value in air quality prediction, building a solid framework for informed decision-making and sustainable development policy formulation.

Deep neural network with empirical mode decomposition and Bayesian optimisation for residential load forecasting

In the context of a resilient energy system, accurate residential load forecasting has become a non-trivial requirement for ensuring effective management and planning strategy/policy development. Due to the highly stochastic nature of energy load profiles, it is difficult to predict accurately, and usually, predictions are error-prone. This paper explores the potential of Empirical Mode Decomposition (EMD) in simplifying the dynamics of complex demand profiles. The simplified components are then embedded within a deep learning model, specifically Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM), to forecast short-term residential loads. The novel modelling framework integrates Bayesian optimisation strategy, feature decomposition technique, feature engineering phase, and percentile-based bias correction algorithm to enhance model accuracy. The model is developed using a case-study residential dwelling located in Fintry (Scotland), and the model performance is assessed over four forecast horizons. The overall efficiency of framework is also investigated for three algorithms: random forest, gradient boosting decision trees (GBDT), and an LSTM network. While EMD and feature engineering were found to greatly improve prediction accuracy, the number of IMFs used was shown to significantly impact the model’s performance and computational complexity. The model was tested on two further case studies from Fintry.

Crop Type Mapping Based on Polarization Information of Time Series Sentinel-1 Images Using Patch-Based Neural Network

Large-scale crop mapping is of fundamental importance to tackle food security problems. SAR remote sensing has lately received great attention for crop type mapping due to its stability in the revisit cycle and is not hindered by cloud cover. However, most SAR image-classification studies focused on the application of backscattering characteristics with machine learning models, while few investigated the potential of the polarization decomposition and deep-learning models. This study investigated whether the radar polarization information mined by polarization decomposition, the patch strategy and the approaches for combining recurrent and convolutional neural networks (Conv2d + LSTM and ConvLSTM2d) could effectively improve the accuracy of crop type mapping. Sentinel-1 SLC and GRD products in 2020 were collected as data sources to extract VH, VV, VH/VV, VV + VH, Entropy, Anisotropy, and Alpha 7-dimensional features for classification. The results showed that the three-dimensional Convolutional Neural Network (Conv3d) was the best classifier with an accuracy and kappa up to 88.9% and 0.875, respectively, and the ConvLSTM2d and Conv2d + LSTM achieved the second and third position. Compared to backscatter coefficients, the polarization decomposition features could provide additional phase information for classification in the time dimension. The optimal patch size was 17, and the patch-based Conv3d outperformed the pixel-based Conv1d by 11.3% in accuracy and 0.128 in kappa. This study demonstrated the value of applying polarization decomposition features to deep-learning models and provided a strong technical support to efficient large-scale crop mapping.

LINK PREDICTION USING TENSOR DECOMPOSITION

In recent years, tensor decomposition has gained increasing interest in the field of link prediction, which aims to estimate the likelihood of new connections forming between nodes in a network. This study highlights the potential of the Canonical Polyadic tensor decomposition in enhancing link prediction in complex networks. It suggests effective tensor decomposition algorithms that not only take into account the structural characteristics of the network but also its temporal evolution. During the process of tensor decomposition, the initial tensor is decomposed into two-way tensors, also known as factor matrices, representing different modes of the data. These factor matrices capture the underlying patterns or relationships within the network, providing insights into the structure and dynamics of the network. For evaluation, we examine a dataset derived from the WSDM. After preprocessing, the data is represented as a multi-way tensor, with each mode representing different aspects such as users, items, and time. Our primary objective is to make precise predictions about the links between users and items within specific time periods. The experimental results demonstrate that our approach significantly improves prediction accuracy for evolving networks, as measured by the AUC.

Waste in the Nineteenth-Century Lyric

ABSTRACT Lingering with waste might seem antithetical for an issue all about newness, but although waste demonstrates persistence, it also indicates change: a body wasting away, an environment becoming a wasteland, a person wasting opportunities or good will. In this essay I argue that the nineteenth-century lyric helps us to think about the accumulation of unusable products, dead ends, waste, as central to intimacy. Percy Shelley’s “The Sensitive-Plant” operates as my case study, a lyric fundamentally troubled by the disposability of the human in contrast to the garden’s continual disturbing survival, and a poem obsessed with the erotic potential of rot and decomposition.

Коррозионная усталость меди в водных средах

The failure of the cooling systems of engines and propellers of the ship poses the serious hazard to passengers and crew. Damage to these units is often caused by corrosion, with localized corrosion being the most dangerous. In the case under consideration, when the metal is simultaneously affected by a corrosive environment and alternating mechanical loads, conditions arise for the behaviour (occurrence) of corrosion fatigue. The prospects of cathodic electrochemical protection of these units are considered in the paper. Since the cooling systems and propellers are made of copper alloys, the effect was studied concerning the potential and composition of the solution on the corrosion of the main component of the structural material — copper. Copper samples with a smooth mechanical pressure concentrator cantilevered in a fluoroplastic electrochemical cell were tested. Alternating bending loads were applied to them, and the number of load cycles before the destruction of the sample (durability) was determined. The experiments were carried out in deaerated solutions of the surfactantly inactive electrolyte NaF, as well as fluoride solutions with additives of surfactants and organic surfactants - benzotriazole. At the same time, an interval of potentials was set, in which the only Faraday process is possible — the reduction of traces of dissolved oxygen. It was found that at potentials less than the zero charge potential, the durability of copper increases significantly. And the introduction of chloride ions and benzotriazole into the solution, on the contrary, leads to a strong decrease in its durability. The potential of zero charge of copper was determined from the measurement data of the differential capacitance of the metal in sodium fluoride solutions with different concentrations of fluoride. The increase in the durability of the metal is explained by the occurrence of hydrogen bonds between the water molecules adsorbed by the hydrogen atoms. This manifests the so-called negative Rebinder effect. For electrochemical protection, the potential range is recommended below the potential of zero charge of the metal, but above the potential of water decomposition. For cooling systems, it is recommended to use deionized water as a coolant.

High carrier mobilities and tunable band structures in two-dimensional [formula omitted]H[formula omitted] ([formula omitted] C, Si, Ge) monolayers

Two-dimensional (2D) materials have injected new possibilities into the field of photocatalysis. In this work, the graphene-like MNH2 (M,N= C, Si, Ge) monolayers are predicted as a series of promising optoelectronic materials by the first-principles calculations. The results show that the MNH2 monolayers have direct bandgaps and the largest electron mobility is 8.15 ×104 cm2 V −1 s−1 for the Si2H2 monolayer, which is obviously higher than that of graphene. The bandgaps are tunable under strains, and the band edges of the Si2H2 and SiGeH2 monolayers can span the reduction potential and oxidation potential of water decomposition to produce hydrogen with specific biaxial strains. Under 4% strains, the solar-to-hydrogen efficiency of the Si2H2 and SiGeH2 monolayers can reach 19.27% and 13.22%, respectively. Thus, the Si2H2 and SiGeH2 monolayers can be predicted as promising photocatalytic materials for the water splitting under visible light irradiation.

First principles study of electronic and optical properties of S-type heterostructures MoSi<sub>2</sub>N<sub>4</sub>/GeC

In this article, the first principles calculation method is used to study the MoSi<sub>2</sub>N<sub>4</sub>/GeC heterostructures, and calculate its structural, electronic, and optical properties. And the effects of different biaxial strains and vertical electric fields on the band structure and optical absorption characteristics of the heterostructures are also investigated. MoSi<sub>2</sub>N<sub>4</sub>/GeC heterostructure is an indirect bandgap semiconductor with a bandgap of 1.25 eV, with the built-in electric field direction pointing from the GeC layer to the MoSi<sub>2</sub>N<sub>4</sub> layer. In addition, its photogenerated carrier transfer mechanism conforms to the S-type heterostructures mechanism, thus improving the oxidation reduction potential of photocatalytic water decomposition, making it fully meet the requirements of photocatalytic water decomposition with pH = 0–14. Under biaxial strain, the band gap first increases and then decreases with the increase of compressive strain, and the light absorption performance in the ultraviolet region increases with compressive strain increasing. The band gap decreases as tensile strain increases, and the light absorption performance in the visible light region is enhanced in comparison with its counterpart under compressive strain. Under a vertical electric field, the band gap increases with positive electric field increasing, and decreases with negative electric field increasing. In summary, MoSi<sub>2</sub>N<sub>4</sub>/GeC heterostructures can be used as an efficient photocatalytic material in some fields such as optoelectronic devices and photocatalysis.

Dual-energy CT based material decomposition to differentiate intrahepatic cholangiocarcinoma from hepatocellular carcinoma

PurposeTo assess the potential of material decomposition in dual-energy CT (DECT) to differentiate intrahepatic cholangiocarcinoma (iCCA) from hepatocellular carcinoma (HCC). MethodIn this retrospective study, we included 94 patients (26 female (27.7 %), median age 64.5 (interquartile range 55.5–74.5) years) with either iCCA or HCC who underwent abdominal contrast-enhanced DECT in arterial phase. To test for differences between iCCA (n = 47) and HCC (n = 47), we evaluated mean attenuation and DECT material density values including iodine density (ID), normalized iodine uptake (NIU), fat fraction, and lesion-to-liver parenchyma ratio. Histopathology served as reference standard for all lesions. We used univariate logistic regression models for the outcome iCCA versus HCC. ROC curve analysis was applied to assess discriminative ability of the model. Model accuracy was evaluated by calculating the Brier score. Youden index was applied to establish thresholds to differentiate between iCCA and HCC. ResultsComparison of quantitative image parameters revealed significant differences between iCCA and HCC for ID (1.6 ± 0.5 mg/ml vs 2.8 ± 0.8 mg/ml, p < 0.001), NIU (14.5 ± 4.8 vs 24.8 ± 10.3, p < 0.001), attenuation (41.9 ± 10.1 HU vs 47.9 ± 8.9 HU, p = 0.003), and fat fraction (12.0 ± 7.8 % vs 9.0 ± 6.4 %, p = 0.045). ROC curve analysis revealed highest ability to differentiate iCCA from HCC for ID (AUC = 0.93, 95 % CI 0.89–0.98). For ID, an optimal threshold of 2.33 mg/dl was determined to discriminate between iCCA and HCC (sensitivity 89.4 %, specificity 76.6 %). ConclusionsDECT-based iodine quantification can serve as a tool for the differentiation of iCCA and HCC in contrast-enhanced CT. ID yielded the highest diagnostic performance and may assist in clinical routine CT diagnostics.

Neural inputs from spinal motor neurons to lateralis vastus muscle: Comparison between sprinters and nonathletes.

The adaptation of neural contractile properties has been observed in previous work. However, the neural changes on the motor unit (MU) level remain largely unknown. Voluntary movements are controlled through the precise activation of MU populations. In this work, we estimate the neural inputs from the spinal motor neurons to the muscles during isometric contractions and characterize the neural adaptation during training by comparing the MU properties decomposed from sprinters and nonathletes. Twenty subjects were recruited and divided into two groups. The high-density surface electromyography (EMG) signals were recorded from the lateralis vastus muscle during the isometric contraction of knee extension and were then decomposed into MU spike trains. Each MU’s action potentials and discharge properties were extracted for comparison across subject groups and tasks. A total of 1097 MUs were identified from all subjects. Results showed that the discharge rates and amplitudes of MUAPs from athletes were significantly higher than those from nonathletes. These results demonstrate the neural adaptations in physical training at the MU population level and indicate the great potential of EMG decomposition in physiological investigations.

MoSi2N4/CrS2 van der Waals heterostructure with high solar-to-hydrogen efficiency

Based on first-principle calculations, electronic properties and photocatalytic performance of MoSi 2 N 4 /CrS 2 heterostructure are studied systematically. Our results show that MoSi 2 N 4 /CrS 2 van der Waals heterostructure possesses an indirect band gap of 1.52 eV. The layer-projected band structures combined with PDOS have confirmed a type-II band alignment, which can effectively reduce the photogenerated carrier recombination. The heterostructure's band edges can cross the redox potential of water decomposition at pH = 0. Compared with two individual monolayers, the optical absorption in visible light region is improved. Moreover, the heterostructure possesses a high solar-to-hydrogen efficiency up to 39.81%. Hence, our results provide useful guidance for MoSi 2 N 4 -based heterostructure design and applications. • MoSi 2 N 4 /CrS 2 vdW heterostructure possesses a type-II band alignment. • MoSi 2 N 4 /CrS 2 vdW heterostructure can be used as photocatalyst for water splitting. • MoSi 2 N 4 /CrS 2 vdW heterostructure has a high STH efficiency (39.81%).

Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data

The aim of this article was to investigate the potential of polarimetric decomposition of Chinese Gaofen-3 (GF-3) C-band fully polarimetric synthetic aperture radar (PolSAR) data for Arctic sea ice classification during summer season. Five different polarimetric decomposition approaches, including the Cloude-Pottier decomposition (Cloude), the Freeman three-component decomposition (Freeman3), the Freeman three-component decomposition using the extended Bragg model (Freeman3X), the Yamaguchi three-component decomposition (Yamaguchi3), and the nonnegative eigenvalue decomposition (NNED) were analyzed using 35 scenes of GF-3 PolSAR data collected over the Fram Strait, Arctic from June 14–18, 2017. Polarimetric features extracted from these five methods were evaluated and utilized to train random forest classifiers to classify open water (calm water and rough water) and sea ice types (melted ice, unmelted ice, and deformed ice). The results show that NNED could ensure physically valid decomposed powers while the other three model-based decompositions had negative values. In terms of sea ice classification, NNED had the highest feature importance scores and achieved an overall accuracy and Kappa coefficient of about 86.18% and 0.82, respectively. Inclusion of radar incidence angle as a feature in the classifier could slightly improve the classification accuracy by about 3%. The influence of incidence angle on sea ice classification accuracy was also investigated and it was found that high incidence angles (39°–46°) were superior to low incidence angles (21°–27°) due to the overall higher accuracies.

On the Potential of Empirical Mode Decomposition for RFI Mitigation in Microwave Radiometry

Radio Frequency Interference (RFI) is an increasing problem particularly for Earth Observation using Microwave Radiometry. RFI has been observed, for example, at L-band by the ESA&#x2019;s SMOS (Soil Moisture and Ocean Salinity) Earth Explorer and by NASA&#x2019;s SMAP (Soil Moisture Active Passive) and Aquarius missions, as well as at C-band by AMSR-E and AMRS-2; and at 10.7 GHz and 18.7 GHz by AMSR-E, AMRS-2, WindSat and GMI [1], [2]. Therefore, systems dedicated to interference detection and removal of contaminated measurements are nowadays a must in order to improve the radiometric accuracy and reduce the loss of spatial coverage caused by interference. In this work, the feasibility of using the Empirical Mode Decomposition (EMD) technique for RFI mitigation is explored. The EMD, also known as Hilbert-Huang Transform (HHT), is an algorithm that decomposes the signal into Intrinsic Mode Functions (IMF). The achieved performance is analyzed, and the opportunities and caveats that this type of methods present are described. EMD is found to be a practical RFI mitigation method, albeit presenting some limitations, and a considerable complexity. Nevertheless, in some conditions, EMD exhibits a better performance than other commonly used methods (such as frequency binning). In particular, it has been found that EMD performs well for RFI affecting the &#x003C; 25% lower part of the Intermediate Frequency (IF) bandwidth.

Electrochemical reduction of tungsten (VI) oxide from a eutectic melt CaCl2-NaCl under potentiostatic conditions

The paper presents results of the study of the electrochemical reduction of tungsten(VI) oxide in a melt of the eutectic composition 52 mol% CaCl2 and 48 mol% NaCl at a liquid gallium electrode. Scanning electron microscopy and X-ray diffraction methods were used to study the microstructures of the obtained powders. The Rietveld method which is based on diffraction patterns were used to calculate the quantitative content of phases in WO3 reduction products. The thermodynamic properties of the electrolysis process were investigated by voltammetry. It is shown that a necessary condition for the electrochemical reduction of WO3 is electrolysis at potentials higher than the standard electrode potential of decomposition of calcium tungstate, which is formed by the interaction of tungsten oxide with calcium chloride. The reduction can take place by both electrochemical and metallothermic mechanisms depending on the conditions of electrolysis. The reduction product is fine tungsten with a particle crystallite size of up to 1 ?m.

Revealing the component structure of the world air transportation network

Air transportation plays an essential role in the global economy. Therefore, there is a great deal of work to understand better the complex network formed by the links between the origins and destinations of flights. Some investigations show that the world air transportation network exhibits a community and a core-periphery structure. Although precious, these representations do not distinguish the inter-regional (global) web of connections from the regional (local) one. Therefore, we propose a new mesoscopic model called the component structure that decomposes the network into local and global components. Local components are the dense areas of the network, and global components are the nodes and links bridging the local components. As a case study, we consider the unweighted and undirected world air transportation network. Experiments show that it contains seven large local components and multiple small ones spatially well-defined. Moreover, it has a main global component covering the world. We perform an extensive comparative analysis of the structure of the components. Results demonstrate the non-homogeneous nature of the world air transportation network. The local components structure highlights regional differences, and the global component organization captures the efficiency of inter-regional travel. Centrality analysis of the components allows distinguishing airports centered on regional destinations from those focused on inter-regional exchanges. Core analysis is more accurate in the components than in the whole network where Europe dominates, blurring the rest of the world. Besides the world air transportation network, this paper demonstrates the potential of the component decomposition for modeling and analyzing the mesoscale structure of networks.