Deconvolution Model Research Articles

Polyolefin microstructural deconvolution methods: The good, the bad, and the ugly

AbstractThe deconvolution of the molecular weight distribution (MWD) of polyolefins into Schultz–Flory most probable distributions has become the standard method to identify the number of site types on multiple‐site‐type olefin polymerization catalysts such as Ziegler–Natta, Phillips, and some supported metallocenes. This method has been used to quantify the effect of polymerization conditions and catalyst formulations on polyolefin MWD and olefin polymerization kinetics. Related methods have also been developed to deconvolute other polyolefin microstructure features, such as the chemical composition and comonomer sequence length distributions. In this paper, I explain the premises behind these deconvolution models and review the publications in this area, highlighting the advantages, disadvantages, and misuses of these methods. I also propose a revised formulation on how to model the MWD of polyolefins made with multiple‐site‐type catalysts using ratio distributions for propagation and chain transfer frequencies. The main objective of this overview article is to highlight the strengths, but also show the pitfalls, of polyolefin microstructure deconvolution methods.

Structure and Luminescence Studies of a Ce3+-Activated Ba5La3MgAl3O15 Green-Emitting Phosphor

Novel green-emitting Ba5La3MgAl3O15:Ce3+ (BLMAO:Ce3+) is successfully obtained by a solid-state reaction. In this study, BLMAO, which is inspired from the Ba6La2A1.5Fe2.5O15 crystal structure, shows a green emission approximately peaked around 500 nm under near-ultraviolet light excitation at 412 nm by Ce3+ doping. Moreover, internal and external quantum efficiencies of BLMAO:0.02Ce3+ are found to be 27 and 22%, respectively. The emission peak deconvolution and Dorenbos model calculation reveal that the Ce3+ ion occupies on two different crystallographic sites. The potential of BLMAO:Ce3+ for phosphor-converted white LEDs (pc-wLEDs) is systematically evaluated from the results of Rietveld refinement and luminescence measurement.

A game theory approach for joint blind deconvolution and inpainting

In this paper we propose a new mathematical model for joint Blind Deconvolution and Inpainting. The main objective is the treatment of blurred images with missing parts, through the game theory framework, in particular, a Nash game, we define two players: Player 1 handles the image intensity while Player 2, operates on the blur kernel. The two engage in a game until the equilibrium is reached. Finally, we provide some numerical examples: we compare the efficiency of our proposed approach to other existing methods in the literature that deals with Blind Deconvolution and Inpainting separately.

A Unifying Approach to Inverse Problems of Ultrasound Beamforming and Deconvolution

Beamforming is an essential step in the ultrasound image formation pipeline and has recently attracted growing interest. An important goal of beamforming is to increase the image spatial resolution, or in other words to narrow down the system Point Spread Function (PSF). In parallel to beamforming approaches, deconvolution methods have also been explored in ultrasound imaging to mitigate the adverse effects of PSF. Unfortunately, these two steps have only been considered separately in a sequential approach. Herein, a novel framework for unifying beamforming and deconvolution in ultrasound image reconstruction is introduced. More specifically, the proposed formulation is a regularized inverse problem including two linear models for beamforming and deconvolution plus additional sparsity constraint. We take advantage of the alternating direction method of multipliers algorithm to find the solution of the joint optimization problem. The performance evaluation is presented on a set of publicly available simulations, real phantoms, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> data. As compared to Delay-And-Sum (DAS) beamforming, simulation results indicate improvements of 45% and 44% in terms of axial and lateral resolution, respectively. Moreover, the proposed method improves the contrast of simulation data by 6.7% in comparison to DAS. The superiority of the proposed approach in comparison with the sequential approach as well as the state-of-the-art beamforming and deconvolution approaches is also shown.

HD-DCDM: Hybrid-domain network for limited-angle computed tomography with deconvolution and conditional diffusion model

HD-DCDM: Hybrid-domain network for limited-angle computed tomography with deconvolution and conditional diffusion model

Wavelet Estimation of Function Derivatives from a Multichannel Deconvolution Model

This paper considers a multichannel deconvolution model with Gaussian white noises. The goal is to estimate the d -th derivatives of an unknown function in the model. For super-smooth case, we construct an adaptive linear wavelet estimator by wavelet projection method. For regular-smooth case, we provide an adaptive nonlinear wavelet estimator by hard-thresholded method. In order to measure the global performances of our estimators, we show upper bounds on convergence rates using the L p -risk ( 1 ≤ p &lt; ∞ ).

Large eddy simulation on unstructured grids using explicit differential filtering: A case study of Taylor-Green vortex

The application of an approximate deconvolution model was extended for large eddy simulation (LES) on unstructured grids by further developing a three dimensional explicit differential filter. Decaying isotropic homogeneous turbulence at a Reynolds number 3,400 was modeled on both structured and unstructured grids. Results were compared to a reference direct numerical simulation and other results reported in the literature. A Taylor-Green vortex at a Reynolds number of 1,600 was also studied as it is a canonical problem for laminar to turbulent transitional flow. Investigative studies on the ADM order and under-relaxation coefficient, the degree of anisotropy of the grid, and the grid resolution were performed to benchmark the filter performance in conjunction with ADM. Finally, a large eddy simulation on a fully unstructured grid was performed to demonstrate the range of applications of the proposed filter. All numerical results showed strong LES numerical stability when the explicit differential filter was used.

Dynamic iterative approximate deconvolution model for large-eddy simulation of dense gas compressible turbulence

We study large-eddy simulation of compressible decaying isotropic turbulence of dense gas at initial turbulent Mach numbers of 0.4 and 0.8. The unclosed subgrid-scale (SGS) terms are approximated by the dynamic iterative approximate deconvolution (DIAD) model proposed by Yuan et al. [“Dynamic iterative approximate deconvolution models for large-eddy simulation of turbulence,” Phys. Fluids 33, 085125 (2021)], and compared with the dynamic Smagorinsky (DSM) model. In an a priori test, the correlation coefficients of the DIAD model for most SGS terms are larger than 0.98, and the relative errors are smaller than 0.2, except for the SGS internal energy flux. In an a posteriori test, the DIAD model can well predict the probability density functions (PDFs) of SGS terms involving thermodynamic variables. Moreover, the DIAD model shows greater advantages than the DSM model in predicting various statistics and structures of compressible turbulence of dense gas, including spectra of velocity and thermodynamic variables, PDFs of SGS kinetic energy flux, deviatoric SGS stress and normalized strain-rate tensor, and the instantaneous spatial structures of vorticity.

Lightweight Multi-Scale Dilated U-Net for Crop Disease Leaf Image Segmentation

Crop disease leaf image segmentation (CDLIS) is the premise of disease detection, disease type recognition and disease degree evaluation. Various convolutional neural networks (CNN) and their modified models have been provided for CDLIS, but their training time is very long. Aiming at the low segmentation accuracy of various diseased leaf images caused by different sizes, colors, shapes, blurred speckle edges and complex backgrounds of traditional U-Net, a lightweight multi-scale extended U-Net (LWMSDU-Net) is constructed for CDLIS. It is composed of encoding and decoding sub-networks. Encoding the sub-network adopts multi-scale extended convolution, the decoding sub-network adopts a deconvolution model, and the residual connection between the encoding module and the corresponding decoding module is employed to fuse the shallow features and deep features of the input image. Compared with the classical U-Net and multi-scale U-Net, the number of layers of LWMSDU-Net is decreased by 1 with a small number of the trainable parameters and less computational complexity, and the skip connection of U-Net is replaced by the residual path (Respath) to connect the encoder and decoder before concatenating. Experimental results on a crop disease leaf image dataset demonstrate that the proposed method can effectively segment crop disease leaf images with an accuracy of 92.17%.

Characteristics of Palu-Koro Fault based on Derivative Analysis and Euler Deconvolution Model of Gravity Data

Indonesia is located at the confluence of three plates, namely the Eurasian, Indo-Australian, and Pacific plates which causes the formation of an active fault line. One of these active faults is the Palu-Koro fault, Sulawesi, with a shift rate of 30-44 mm/year. A geophysical method such as gravity determines the fault’s characteristics. Gravity data is processed by a derivative analysis process in the form of First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD), which is carried out to represent the type of fault, and the Euler Deconvolution method is used to determine the location and depth of the fault anomaly source. The results show that the Palu-Koro fault has a high anomaly value in the west of 0.00557-0.01087 mGal in the north-south direction on the FHD map and an anomaly value of 0 in the north-south direction on the SVD map. The source of the anomaly can be seen more clearly in the results of the Euler Deconvolution modelling at a depth of 50-400 m from the topography of the study area. The Palu-Koro fault is proven to have many structures at shallow depths with very active movements that can trigger earthquakes.

Visualization of the Dynamic Brain Activation Pattern during a Decision-Making Task.

Decision making is a complex process involving various parts of the brain which are active during different times. It is challenging to measure externally the exact instant when any given region becomes active during the decision-making process. Here, we propose the development and validation of an algorithm to extract and visualize the dynamic functional brain activation information from the observed fMRI data. We propose the use of a regularized deconvolution model to simultaneously map various activation regions within the brain and track how different activation regions changes with time, thus providing both spatial and temporal brain activation information. The proposed technique was validated using simulated data and then applied to a simple decision-making task for identification of various brain regions involved in different stages of decision making. Using the results of the dynamic activation for the decision-making task, we were able to identify key brain regions involved in some of the phases of decision making. The visualization aspect of the algorithm allows us to actually see the flow of activation (and deactivation) in the form of a motion picture. The dynamic estimate may aid in understanding the causality of activation between various brain regions in a better way in future fMRI brain studies.

Model training periods impact estimation of COVID-19 incidence from wastewater viral loads

Wastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective responses. As the wastewater (WW) becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision-making. This research aimed to develop and implement a mathematical framework to infer incident cases of COVID-19 from SARS-CoV-2 levels measured in WW. We propose a classification scheme to assess the adequacy of model training periods based on clinical testing rates and assess the sensitivity of model predictions to training periods. A testing period is classified as adequate when the rate of change in testing is greater than the rate of change in cases. We present a Bayesian deconvolution and linear regression model to estimate COVID-19 cases from WW data. The effective reproductive number is estimated from reconstructed cases using WW. The proposed modeling framework was applied to three Northern California communities served by distinct WW treatment plants. The results showed that training periods with adequate testing are essential to provide accurate projections of COVID-19 incidence.

Identification of Human Global, Tissue and Within-Tissue Cell-Specific Stably Expressed Genes at Single-Cell Resolution.

Stably Expressed Genes (SEGs) are a set of genes with invariant expression. Identification of SEGs, especially among both healthy and diseased tissues, is of clinical relevance to enable more accurate data integration, gene expression comparison and biomarker detection. However, it remains unclear how many global SEGs there are, whether there are development-, tissue- or cell-specific SEGs, and whether diseases can influence their expression. In this research, we systematically investigate human SEGs at single-cell level and observe their development-, tissue- and cell-specificity, and expression stability under various diseased states. A hierarchical strategy is proposed to identify a list of 408 spatial-temporal SEGs. Development-specific SEGs are also identified, with adult tissue-specific SEGs enriched with the function of immune processes and fetal tissue-specific SEGs enriched in RNA splicing activities. Cells of the same type within different tissues tend to show similar SEG composition profiles. Diseases or stresses do not show influence on the expression stableness of SEGs in various tissues. In addition to serving as markers and internal references for data normalization and integration, we examine another possible application of SEGs, i.e., being applied for cell decomposition. The deconvolution model could accurately predict the fractions of major immune cells in multiple independent testing datasets of peripheral blood samples. The study provides a reliable list of human SEGs at the single-cell level, facilitates the understanding on the property of SEGs, and extends their possible applications.

The effect of sub-filter scale dynamics in large eddy simulation of turbulence

We study the effect of sub-filter scale (SFS) dynamics on the accuracy of direct deconvolution models (DDM) in large eddy simulation of isotropic turbulence at different filter-to-grid ratios (FGR), by using several types of invertible filters including the Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. We show that the FGR is crucial in controlling errors to ensure an accurate prediction of SFS stresses. In the case of FGR of 1, the DDM models cannot accurately reconstruct SFS stress, since the effect of SFS dynamics on SFS stress is not properly resolved by the coarse grid. The prediction abilities of most DDM models are significantly improved at FGR of 2, giving rise to quite an accurate reconstruction of SFS stresses, except for the situation of Helmholtz I and II filters. All the DDM models give very accurate results at FGR of 4. Moreover, the DDM models are comprehensively compared against various traditional SFS models, including the velocity gradient model, dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and the approximate deconvolution model. In the a priori study, the correlation coefficients of SFS stress for the DDM are much larger than those of the traditional models. In the a posteriori study, DDM outperforms DSM and DMM models in the prediction of various velocity statistics and instantaneous flow structures. These results indicate that the DDM framework with an appropriate FGR has much potential in developing high-fidelity SFS models in the LES of turbulence.

Dual hybrid convolutional generative adversarial network for hyperspectral image classification

ABSTRACT Generative adversarial networks (GANs) have effectively promoted the development of hyperspectral image classification technology in generating samples. Many GAN-based models for hyperspectral image classification use deconvolution to generate fake samples, which will cause chequerboard artefacts and affect classification performance. Furthermore, the training of GANs still faces the problem of mode collapse. Aiming at the above problems, we proposed a dual hybrid convolutional generative adversarial network (DHCGAN) for hyperspectral image classification. Firstly, the combination of nearest neighbour upsampling and sub-pixel convolution is employed in the generator, which avoids the overlap of convolution domain and effectively suppresses the chequerboard artefacts caused by deconvolution. Secondly, the traditional convolution and dilated convolution are fused in the discriminator, which expands the receptive field without increasing parameters and achieves more effective feature extraction. In addition, some adaptive drop blocks are embedded into the generator and discriminator to effectively alleviate the problem of mode collapse. Experiments were performed on four hyperspectral datasets (including three classical datasets – Indian Pines, University of Pavia and Houston, a new dataset – WHU-Hi-HanChuan). Experimental results show that the proposed method can provide a certain performance improvement over some competing methods, such as the accuracy has been increased by more than 1% on the three classical datasets, and even got over 3% improvement on WHU_Hi_HanChaun dataset.

Noise-robust phase-space deconvolution for light-field microscopy.

.SignificanceLight-field microscopy has achieved success in various applications of life sciences that require high-speed volumetric imaging. However, existing light-field reconstruction algorithms degrade severely in low-light conditions, and the deconvolution process is time-consuming.AimThis study aims to develop a noise robustness phase-space deconvolution method with low computational costs.ApproachWe reformulate the light-field phase-space deconvolution model into the Fourier domain with random-subset ordering and total-variation (TV) regularization. Additionally, we build a time-division-based multicolor light-field microscopy and conduct the three-dimensional (3D) imaging of the heart beating in zebrafish larva at over 95 Hz with a low light dose.ResultsWe demonstrate that this approach reduces computational resources, brings a tenfold speedup, and achieves a tenfold improvement for the noise robustness in terms of SSIM over the state-of-the-art approach.ConclusionsWe proposed a phase-space deconvolution algorithm for 3D reconstructions in fluorescence imaging. Compared with the state-of-the-art method, we show significant improvement in both computational effectiveness and noise robustness; we further demonstrated practical application on zebrafish larva with low exposure and low light dose.

Magnetic anomaly patterns and volcano-tectonic features associated with geothermal prospect areas in the Ziway-Shala Lakes Basin, Central Main Ethiopian Rift

The Ziway-Shala Lakes Basin is a structural depression within the Main Ethiopian rift that is associated with Cenozoic volcanism, faulting and sedimentation. The Aluto-Langano volcano complex contains a geothermal field within this basin that is currently being exploited for geothermal power production. Recent magnetotelluric analyzes did not reveal a heat source beneath the Aluto-Langano geothermal field but found an electrical conductor that may be the magmatic heat source near the Silti Debre Zeyet Fault Zone (SDFZ) northwest of the Aluto- Langano geothermal field. To investigate the subsurface structure of the Ziway-Shala Lakes Basin and especially the Aluto-Langano geothermal field, a ground based magnetic survey was performed. Volcano-tectonic features were identified using a differentially reduced to the pole total magnetic field anomaly and a variety of map transformations including upward continuation, first vertical derivatives and tilt derivatives, to map the regional and residual magnetic fields. To identify subsurface magnetic susceptibility sources between the SDFZ and the Aluto-Shala geothermal field, a power spectral analysis, 2D-Werner Deconvolution, and 2D forward modeling were applied. The integrated interpretation of the maps and models, indicates that magnetic maxima occurred over the SDFZ and the Wonji Fault Zone and the minima over the Aluto-Langano geothermal field. The magnetic maxima may be caused by shallow (< 5 km) igneous intrusions that are related to deeper magma movement. The magnetic minima may be caused by warm geothermal waters causing thermal alteration of the magnetic minerals under the Aluto-Langano volcanic complex. The residual and derivative magnetic anomaly maps suggest the existence of lineations that may transfer geothermal fluids suggested by the magnetotelluric models from the SDFZ to the Aluto-Langano geothermal field. The upward movement of the fluids is evident by the magnetic minimum in the Aluto-Langano geothermal field and agrees with a recent earthquake analysis. Additionally, a majority of the magnetic lineaments are oriented parallel to pre-existing Mesozoic structures related to the opening of the Ogaden Basin.

Density-unweighted subgrid-scale models for large-eddy simulations of compressible turbulence

Density-unweighted methods in large-eddy simulations (LES) of turbulence have received little attention, and the modeling of unclosed terms using density-unweighted methods even less. We investigate the density-unweighted subgrid-scale (SGS) closure problem for LES of decaying compressible isotropic turbulence at initial turbulent Mach numbers 0.4 and 0.8. Compared to the LES with Favre (density-weighted) filtering, there are more unclosed SGS terms for density-unweighted LES, which can be reconstructed using different SGS models, including the gradient model (GM), approximate deconvolution model (ADM), dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and the dynamic iterative approximate deconvolution (DIAD) models proposed by Yuan et al. “Dynamic iterative approximate deconvolution models for large-eddy simulation of turbulence,” Phys. Fluids 33, 085125 (2021). We derive GM models suitable for density-unweighted methods. We also, for the first time, apply the DIAD model to investigate compressible turbulence. In the a priori tests, the correlation coefficients of the GM, ADM, and DIAD models are larger than 0.9. Particularly, the correlation coefficients of DIAD models exceed 0.98 and the relative errors are below 0.2, which is superior to that in other SGS models. In the a posteriori tests of the density-unweighted LES, the DIAD model shows great advantages over other SGS models (including GM, ADM, DSM, and DMM models) in predicting the various statistics and structures of compressible turbulence, including the velocity spectrum, probability density functions (PDFs) of SGS fluxes and the instantaneous spatial structures of SGS heat flux, SGS kinetic energy flux, and vorticity.

An ultrasensitive approach for cancer screening and tissue of origin prediction based on targeted methylation sequencing of cell-free DNA.

10553 Background: Cancer-specific therapy requires the identification of the tumor origin. Detecting cancer and identifying the tumor origin in early stage can improve the survival and prognosis of cancer patients. However, early diagnosis of cancer is challenging as the early symptoms of cancer are mild or even absent. In order to improve the diagnostic efficacy and the patient compliance, we developed a two-stage model for cancer diagnosis and tissue of origin (TOO) classification, using DNA methylation profile evaluated through non-invasive blood-based testing as biomarker. Methods: cfDNA samples from 302 healthy and 598 cancer patients of 8 cancer types (colorectal, lung, liver, pancreatic, gastric, esophagus, breast and ovarian) were sequenced by our in-house-designed DNA methylation panel and randomly split into a training dataset (213:426) and a testing dataset (89:172). The testing dataset was reserved to evaluate the performance of models and would not be used during the feature selection and model development process. Methylation levels of cfDNA samples were measured by the methylated fragment ratios (MFRs) in methylated-correlated blocks (MCBs). We first selected pan-cancer markers by comparing the MFR values of MCBs between the healthy and the diseased individuals and developed a binary classifier (hyper-methylation score). Sample correctly predicted by the hyper-methylation score in the cancer group were then used for TOO marker selection and model fitting. TOO biomarkers were obtained by a series of pairwise comparisons of MFR values between any two cancer types. A deconvolution model was built to estimate the composition of ctDNA, which implied the tumor origin. Results: The hyper-methylation score model based on 135 MCB biomarkers achieved an area under the curve (AUC) of 0.89 in the training dataset and 0.85 in the testing dataset. Under a specificity of 94.8%, sensitivity was 66.2% in the training dataset. Using the 282 cases successfully being predicted, 583 TOO markers were selected to build the deconvolution model. In the testing dataset, the specificity was 95.5%, and the overall sensitivity was 66.3%. With increasing stage, sensitivity increased in all cancer types: 34.6%, 57.1%, 62.5% and 84.8% in stage I, II, III and IV respectively. In stage I-III, sensitivity was 52.3%. TOO classes were predicted in the 114 true positives from the hyper-methylation score model, producing a top 1 accuracy (true class matched the most probable class) of 75.4% and a top 2 accuracy (true class matched the first or the second most probable class) of 84.2%. Top 1 accuracy was 71.1% in stage I-III, vs. 84.0% in stage IV. Top 2 accuracy was 82.2% in stage I-III, vs. 90.0% in stage IV. Conclusions: Our cfDNA-based epigenetic method achieved outstanding performance either in pan-cancer detection or in TOO classification, and is a promising tool for early-stage cancer diagnosis.

Noise modelling of perfusion CT images for robust hemodynamic parameter estimations

Objective. The radiation dose of cerebral perfusion computed tomography (CPCT) imaging can be reduced by lowering the milliampere-second or kilovoltage peak. However, dose reduction can decrease image quality due to excessive x-ray quanta fluctuation and reduced detector signal relative to system electronic noise, thereby influencing the accuracy of hemodynamic parameters for patients with acute stroke. Existing low-dose CPCT denoising methods, which mainly focus on specific temporal and spatial prior knowledge in low-dose CPCT images, not take the noise distribution characteristics of low-dose CPCT images into consideration. In practice, the noise of low-dose CPCT images can be much more complicated. This study first investigates the noise properties in low-dose CPCT images and proposes a perfusion deconvolution model based on the noise properties. Approach. To characterize the noise distribution in CPCT images properly, we analyze noise properties in low-dose CPCT images and find that the intra-frame noise distribution may vary in the different areas and the inter-frame noise also may vary in low-dose CPCT images. Thus, we attempt the first-ever effort to model CPCT noise with a non-independent and identical distribution (i.i.d.) mixture-of-Gaussians (MoG) model for noise assumption. Furthermore, we integrate the noise modeling strategy into a perfusion deconvolution model and present a novel perfusion deconvolution method by using self-relative structural similarity information and MoG model (named as SR-MoG) to estimate the hemodynamic parameters accurately. In the presented SR-MoG method, the self-relative structural similarity information is obtained from preprocessed low-dose CPCT images. Main results. The results show that the presented SR-MoG method can achieve promising gains over the existing deconvolution approaches. In particular, the average root-mean-square error (RMSE) of cerebral blood flow (CBF), cerebral blood volume, and mean transit time was improved by 40.3%, 69.1%, and 40.8% in the digital phantom study, and the average RMSE of CBF can be improved by 81.0% in the clinical data study, compared with tensor total variation regularization deconvolution method. Significance. The presented SR-MoG method can estimate high-accuracy hemodynamic parameters andachieve promising gains over the existing deconvolution approaches.