Smooth Component Research Articles

Smooth robust principal component analysis based on multidimensional transform tensor for dynamic MRI

Dynamic magnetic resonance imaging (DMRI) stands as a sophisticated medical imaging technique pivotal to clinical practice, but the protracted duration of its imaging poses a substantial constraint on its practical application. This paper introduces a smooth robust principal component analysis model based on multidimensional transform tensors for accelerating DMR imaging. Specifically, the proposed method breaks down data into low-rank and sparse parts for reconstruction, respectively. The low-rank part employs a multidimensional adaptive transformation framework to generate transform tensors with favorable low-rank properties along three dimensions of DMR data. As for the sparse part, precise reconstruction can be achieved with the sparsity of the data after sparse transformation. In addition, to enhance the preservation of image details, this paper introduces a novel weighted tensor total variation regularization, imposing varying degrees of constraints based on smoothness in different dimensions. Experimental results demonstrate that the proposed method realizes superior reconstruction effects in comparison to existing advanced methods.

Space–time adaptive ADER-DG finite element method with LST-DG predictor and a posteriori sub-cell ADER-WENO finite-volume limiting for multidimensional detonation waves simulation

The space–time adaptive ADER–DG finite element method with LST–DG predictor and a posteriori sub–cell ADER–WENO finite–volume limiting was used for simulation of multidimensional reacting flows with detonation waves. The presented numerical method does not use any ideas of splitting or fractional time steps methods. The modification of the LST–DG predictor has been developed, based on a local partition of the time step in cells in which strong reactivity of the medium is observed. This approach made it possible to obtain solutions to classical problems of flows with detonation waves and strong stiffness, without significantly decreasing the time step. The results obtained show the very high applicability and efficiency of using the ADER–DG–PN method with a posteriori sub–cell limiting for simulating reactive flows with detonation waves. The numerical solution shows the correct formation and propagation of ZND detonation waves. The structure of detonation waves is resolved by this numerical method with subcell resolution even on coarse spatial meshes. The smooth components of the numerical solution are correctly and very accurately reproduced by the numerical method. Non–physical artifacts of the numerical solution, typical for problems with detonation waves, such as the propagation of non–physical shock waves and weak detonation fronts ahead of the main detonation front, did not arise in the results obtained. The results of simulating rather complex problems associated with the propagation of detonation waves in significantly inhomogeneous domains are presented, which show that all the main features of detonation flows are correctly reproduced by this numerical method. It can be concluded that the space–time adaptive ADER–DG–PN method with LST–DG predictor and a posteriori sub–cell ADER–WENO finite–volume limiting is perfectly applicable to simulating fairly complex reacting flows with detonation waves.

A novel coupled p(x) and fractional PDE denoising model with theoretical results

In this paper, we formulate a new coupled PDE-based configuration for image denoising. We elaborate a new class of coupled PDE that involves a p ( x ) -Laplace and a controlled fractional-type operator, which takes into account the texture and smooth components during the recovering process. We give some essential theoretical results and we establish the well-posedness of the suggested coupled equation based on Galerkin approximation. Finally, we perform a fully discretization part of the system and illustrate some various numerical realizations to ensure the efficiency of our coupled PDE by conducting some comparison experiments against state of art PDE denoising models.

An optimal algorithm for minimax problems with smooth components

An optimal algorithm for minimax problems with smooth components

Lithium-Ion Battery Health State Estimation Based on Feature Reconstruction and Optimized Least Squares Support Vector Machine

Abstract The state of health (SOH) of a battery is the main indicator of battery life. In order to improve the SOH estimation accuracy, a model framework for lithium-ion battery health state estimation with feature reconstruction and improved least squares support vector machine is proposed. First, the indirect health features (HF) are obtained by processing multiple health features extracted from the charging and discharging phases through principal component analysis to remove the information redundancy among multiple features. Subsequently, multiple smooth component subsequences of different frequencies are obtained by using variational modal decomposition to efficiently capture the overall downtrend and regeneration fluctuations of the data. Then, use the sparrow search algorithm to optimize the least squares support vector machine to build an estimation model, predict and superimpose the reconstructed fusion features of multiple feature subsequences. Finally, use the mapping relationship between the reconstructed HF and the SOH for the estimation. The NASA battery dataset and the University of Maryland battery dataset (CACLE) are used to perform validation tests on multiple batteries with different cycle intervals. The results show that the mean absolute error and root mean square error are less than 1% and the method has high-estimation accuracy and robustness.

A variable p[u] exponent reaction-diffusion PDE for image denoising

The paper presents a unique reaction-diffusion coupled system with p(u(t,x))-growth, employing the decomposition approach of H−1 norm. This method is designed to address image denoising by considering both texture and smooth components during the recovery process. The existence and uniqueness of weak solutions for the coupled system are established using Galerkin's method within a suitable space framework. Experimental analysis demonstrates the model's effectiveness in image restoration, and it is compared with other competitive models to showcase its performance.

The endocrine disruptor vinclozolin causes endothelial injury via eNOS/Nox4/IRE1α signaling

Vinclozolin (VCZ) is a common dicarboximide fungicide used to protect crops from diseases. It is also an endocrine disruptor, and its effects on various organs have been described but its influence on vasculature has not yet been addressed. This study focuses on the potential mechanism of VCZ-induced vascular injury. The effect of VCZ on vascular function in terms of relaxing and contracting response was evaluated in mice aorta. A short exposure to VCZ affected the endothelial but not the smooth muscle component. Specifically, it caused a disruption of the eNOS/NO signaling. In line, a short exposure to VCZ in bovine aortic endothelial cells promoted eNOS uncoupling resulting in a reduction of NO bioavailability and eNOS dimer/monomer ratio, and in turn an increase of nitro-tyrosine levels and ROS formation. Prolonging the exposure to VCZ (3 and 6h) an up-regulation of Nox4, enzyme-generating ROS constitutively expressed in endothelial cells, and an increase in ROS and malondialdehyde content coupled with a reduction in NO levels were found. These events were strictly linked to endoplasmic reticulum stress as demonstrated by the phosphorylation of inositol-requiring transmembrane kinase endoribonuclease 1α (IRE1α), a stress sensor and its reversion by using a selective inhibitor. Collectively, these results demonstrated that VCZ provokes endothelial dysfunction by oxidative stress involving eNOS/Nox4/IRE1α axis. The rapid exposure affected the endothelial function promoting eNOS uncoupling while a post-transcriptional modification, involving Nox4/IRE1α signaling, occurred following prolonged exposure. Thus, exposure to VCZ could contribute to the onset and/or progression of cardiovascular diseases associated with endothelial dysfunction.

Magnetic resonance image reconstruction based on image decomposition constrained by total variation and tight frame.

Magnetic resonance imaging (MRI) is a commonly used tool in clinical medicine, but it suffers from the disadvantage of slow imaging speed. To address this, we propose a novel MRI reconstruction algorithm based on image decomposition to realize accurate image reconstruction with undersampled k-space data. In our algorithm, the MR images to be recovered are split into cartoon and texture components utilizing image decomposition theory. Different sparse transform constraints are applied to each component based on their morphological structure characteristics. The total variation transform constraint is used for the smooth cartoon component, while the L0 norm constraint of tight frame redundant transform is used for the oscillatory texture component. Finally, an alternating iterative minimization approach is adopted to complete the reconstruction. Numerous numerical experiments are conducted on several MR images and the results consistently show that, compared with the existing classical compressed sensing algorithm, our algorithm significantly improves the peak signal-to-noise ratio of the reconstructed images and preserves more image details. Our algorithm harnesses the sparse characteristics of different image components to reconstruct MR images accurately with highly undersampled data. It can greatly accelerate MRI speed and be extended to other imaging reconstruction fields.

Parameter inference for degenerate diffusion processes

We study parametric inference for ergodic diffusion processes with a degenerate diffusion matrix. Existing research focuses on a particular class of hypo-elliptic Stochastic Differential Equations (SDEs), with components split into ‘rough’/‘smooth’ and noise from rough components propagating directly onto smooth ones, but some critical model classes arising in applications have yet to be explored. We aim to cover this gap, thus analyse the highly degenerate class of SDEs, where components split into further sub-groups. Such models include e.g. the notable case of generalised Langevin equations. We propose a tailored time-discretisation scheme and provide asymptotic results supporting our scheme in the context of high-frequency, full observations. The proposed discretisation scheme is applicable in much more general data regimes and is shown to overcome biases via simulation studies also in the practical case when only a smooth component is observed. Joint consideration of our study for highly degenerate SDEs and existing research provides a general ‘recipe’ for the development of time-discretisation schemes to be used within statistical methods for general classes of hypo-elliptic SDEs.

BARREL Observations of Microburst Events With a Slowly‐Varying Component

AbstractElectron microburst precipitation has been shown to have significant potential for depletion of the outer radiation belt. We present observations from the Balloon Array for Radiation belt Relativistic Electron Losses (BARREL) of six (five unique and one dual‐balloon observation) microburst events, each containing minutes to hours of persistent microbursts. We find that each event included a slowly‐varying smooth precipitation component underlying the bursty component. The smooth component has not yet been fully characterized in the literature; we have written a program to identify microburst events and quantify the relative contributions of each component in the BARREL data. In all six events analyzed, the smooth component contributed more to the total X‐ray counts measured, indicating that the smooth component could contribute significantly to radiation belt loss.

Automated tight Lyapunov analysis for first-order methods

AbstractWe present a methodology for establishing the existence of quadratic Lyapunov inequalities for a wide range of first-order methods used to solve convex optimization problems. In particular, we consider (i) classes of optimization problems of finite-sum form with (possibly strongly) convex and possibly smooth functional components, (ii) first-order methods that can be written as a linear system on state-space form in feedback interconnection with the subdifferentials of the functional components of the objective function, and (iii) quadratic Lyapunov inequalities that can be used to draw convergence conclusions. We present a necessary and sufficient condition for the existence of a quadratic Lyapunov inequality within a predefined class of Lyapunov inequalities, which amounts to solving a small-sized semidefinite program. We showcase our methodology on several first-order methods that fit the framework. Most notably, our methodology allows us to significantly extend the region of parameter choices that allow for duality gap convergence in the Chambolle–Pock method when the linear operator is the identity mapping.

Solar and Wind Data Recognition: Fourier Regression for Robust Recovery

Accurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intelligence are being leveraged to enhance forecasting methodologies, enabling more accurate predictions and optimized decision-making capabilities. Integrating these novel paradigms improves forecasting accuracy, fostering a more efficient and reliable energy grid. These advancements allow better demand management, optimize resource allocation, and improve robustness to potential disruptions. The data collected from solar intensity and wind speed is often recorded through sensor-equipped instruments, which may encounter intermittent or permanent faults. Hence, this paper proposes a novel Fourier network regression model to process solar irradiance and wind speed data. The proposed approach enables accurate prediction of the underlying smooth components, facilitating effective reconstruction of missing data and enhancing the overall forecasting performance. The present study focuses on Midland, Texas, as a case study to assess direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and wind speed. Remarkably, the model exhibits a correlation of 1 with a minimal RMSE (root mean square error) of 0.0007555. This study leverages Fourier analysis for renewable energy applications, with the aim of establishing a methodology that can be applied to a novel geographic context.

Повышение адекватности моделей электромеханотронных систем

Development of electromechanotronic systems is connected with modeling and calculation of processes of various kinds. Electromagnetic, mechanical and thermal processes are analyzed, control, regulation, protection, monitoring and diagnostics problems are solved. Systems may contain a large number of power semiconductor elements. Short-term and multi-hour processes are calculated. Taking into account mutual connections of processes of various kinds in calculations of complex systems requires inadmissibly large expenditures of machine time. The problems are solved under more or less significant assumptions, which leads to a decrease in the adequacy of the results. The methods of systems calculation are proposed, which allow to reduce machine time consumption and to use more detailed description of installations and more accurate computer models. Possibilities of calculation of processes by «smooth components» of variables, and also application of dual models, in which speed and accuracy are combined, are considered. As an example, a system with an active cascade frequency converter containing 780 transistors and diodes, parasitic inductances and snubber circuits, and a vector-controlled induction motor with ladder circuits accounting for current displacement is analyzed. The model takes into account energy losses in the converter and in the motor, as well as the distribution of losses by components. It also takes into account emergency operation modes associated with disconnection of a part of low-voltage units of the cascade converter and maintenance of power quality at its input and output by symmetrization of the main current components and suppression of parasitic components.

Adenomatoid tumor with extensive reactive smooth muscle: a diagnostic dilemma

Abstract Introduction/Objective Adenomatoid tumors are slow-growing benign tumors of mesothelial origin. They comprise 30% of all tumors of the paratesticular area. In this location, they commonly involve the epididymis, spermatic cord, ejaculatory duct and rarely testicular parenchyma. Histologically, a spectrum of growth patterns can be seen, such as adenoid, tubular, glandular, solid, or cystic. In some cases, a smooth muscle component can also be present. The purpose of this report is to describe a case of adenomatoid tumor with exuberant smooth muscle component, mimicking leiomyoma. Methods/Case Report A 28-year-old male presented with painless left inguinal bulge. He noticed it five years prior, and it had been slowly growing since then. Examination revealed a paratesticular mass which was subsequently excised. Grossly, the tumor was firm with smooth, white-tan cut surfaces. Microscopically, the tumor was well- circumscribed, showing predominance of smooth muscle bundles and rare cuboidal to ovoid cells in tubular growth pattern. In the background there was little intervening stroma. Immunohistochemical stains showed the cuboidal cells to be positive for WT1 and calretinin, confirming the mesothelial origin. Diagnosis of adenomatoid tumor was rendered. In contrast to adenomatoid tumors, leiomyomas are whiter and firmer on gross examination and lack the characteristic tubules or cystic spaces lined by mesothelial cells. Results (if a Case Study enter NA) N/A Conclusion Exuberant smooth muscle differentiation in adenomatoid tumor can mislead to diagnosis of leiomyoma. Careful inspection for tubules of mesothelial origin and appropriate immunohistochemical stains are key to accurate diagnosis. Pathologists need to be aware of this potential pitfall.

CTDNet: cartoon-texture decomposition-based gray image super-resolution network with multiple degradations

In the case of multiple degradations, current deep-learning-based gray image super-resolution (SR) methods equally process all components in an image, resulting in missing subtle details. To address this issue, we elaborate a cartoon-texture decomposition-based (CTD) module that can automatically decompose an image into a smooth cartoon component and an oscillatory texture component. The CTD module is a plug-and-play prior module that can be applied in solving imaging inverse problems. Specifically, for the SR task under multiple degradations, we apply CTD as a prior module to build an unfolding SR network termed CTDNet. For the SR task of real terahertz images, the boundary (i.e., the boundary between the object of interest and the carrier table) recovered by CTDNet has artifacts, which limits its realistic applications. To reduce these boundary artifacts, we post-process the SR terahertz images by using a boundary artifact reduction method. Experimental results on the synthetic dataset and real terahertz images demonstrate that the proposed algorithms can maintain subtle details and achieve comparable visual results. The code can be found at https://github.com/shibaoshun/CTDNet.

Malignant female genital tract smooth muscle tumors with adipocytic differentiation: A morphologic, immunohistochemical, MDM2 fluorescence in situ hybridization and molecular genetic study of 6 lipoleiomyosarcomas

Leiomyosarcoma with adipocytic differentiation or lipoleiomyosarcoma is an uncommon sarcoma of the female genital tract with only a few individual reports in the literature. We therefore performed a morphologic, immunohistochemical, MDM2 gene amplification and RNA and DNA sequencing analysis of a series of gynecologic lipoleiomyosarcoma to better define the clinicopathologic spectrum. Six tumors from 6 patients were identified and classified as spindled lipoleiomyosarcoma (n = 2), mixed spindled and myxoid lipoleiomyosarcoma (n = 1), epithelioid lipoleiomyosarcoma with focal myxoid features (n = 1) and mixed spindled and epithelioid lipoleiomyosarcoma (n = 2). Patient age ranged from 41 to 64 years (mean: 49; median: 50). Primary location included uterine corpus (3), uterine corpus/cervix (2) and broad ligament (1). Tumor size ranged from 4.5 to 22 cm (mean: 11.2; median: 9.8). Four patients had metastasis at presentation or subsequently developed recurrent or distant disease. Patient status was known for 5: 2 dead of disease, 2 alive with disease and 1 alive without evidence of disease. Immunohistochemical expression of smooth muscle markers, ER, PR and WT-1 showed patterns similar to non-adipocytic gynecologic leiomyosarcomas. MDM2 amplification fluorescence in situ hybridization performed on 2 tumors was negative in 1 and equivocal in 1. Sequencing studies performed on 3 tumors found TP53 mutations in 3, with 1 tumor also having an ATRX alteration. No gene fusions were identified. Although lipoleiomyosarcomas have a diverse morphologic spectrum, our findings suggest the smooth muscle component shares morphologic and immunohistochemical features with female genital tract non-adipocytic leiomyosarcomas. Lipoleiomyosarcomas also have genetic alterations associated with non-adipocytic gynecologic leiomyosarcomas.

Torsion divisors of plane curves and Zariski pairs

This paper is devoted to the study of the embedded topology of reducible plane curves having a smooth irreducible component. In previous studies, the relationship between the topology and certain torsion classes in the Picard group of degree zero of the smooth component was implicitly considered. Here this relationship is formulated clearly and a criterion is given for distinguishing the embedded topology in terms of torsion classes. Furthermore, a method is presented for systematically constructing examples of curves where this criterion is applicable, and new examples of Zariski N N -tuples are produced.

Anatomically compliant modes of variations: New tools for brain connectivity.

Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects' expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients.

Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion

The multiple loads of the Regional Integrated Energy System (RIES) possess characteristics of randomness and relatively higher complexity. The current forecasting methods struggle to effectively handle the non-stationary sequence of these multiple loads, leading to less accurate load forecasting. To address this problem, this paper proposes a multi-model fusion prediction method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD), Genetic Algorithm-Long Short Term Memory (GA-LSTM), Radial Basis Fusion-Autoencoder (RBF-AE), and Particle Swarm Optimization-Support Vector Machine (PSO-SVM). First, the load sequence is decomposed into different frequency Intrinsic Mode Functions (IMFs) components using CEEMD. The IMFs components are then grouped based on their zero-crossing rate and Sample Entropy (SE), resulting in three distinct groups: high-, medium-, and low-frequency components. Next, the high-frequency load component, which exhibit strong randomness, are predicted using GA-LSTM. The medium-frequency load component, which have weaker randomness, are predicted using RBF-AE. The smooth and periodic low-frequency load component are predicted using PSO-SVM. The prediction results from these three models are reconstructed to obtain the final predictive value. Finally, experimental results confirm that the forecasting model can effectively handle non-stationary load sequences and demonstrate the highest level of forecasting accuracy.

The influence of subhaloes on host halo properties

ABSTRACT Within the ΛCDM cosmology, dark matter haloes are composed of both a smooth component and a population of smaller gravitationally bound subhaloes. These components are often treated as a single halo when properties, such as density profiles, are extracted from simulations. Recent work has shown that density profiles change substantially when subhalo mass is excluded. In this paper, we expand on this result by analysing three specific host halo properties – concentration (cNFW), spin (λB), and shape (c/a) – when calculated only from the smooth component of the halo. This analysis is performed on both Milky Way-mass haloes and cluster-mass haloes in high-resolution zoom-in N-body simulations. We find that when subhaloes are excluded, the median value of (1) cNFW is enhanced by $\approx 30\pm 11$ and $\approx 77\pm 8.1~{{\ \rm per\ cent}}$ for Milky Way-mass ($10^{12.1}\, \text{M}_\odot$) and cluster-mass ($10^{14.8}\, \text{M}_\odot$) haloes, respectively, (2) λB is reduced for Milky Way-mass by $\approx 11\pm 4.9~{{\ \rm per\ cent}}$ and cluster-mass haloes by $\approx 27\pm 3.5~{{\ \rm per\ cent}}$. Additionally, with the removal of subhaloes, cluster-mass haloes tend to become more spherical as the ratio of minor-to-major axis, c/a, increases by $\approx 11\pm 3.6~{{\ \rm per\ cent}}$, whereas Milky Way-mass haloes remain approximately the same shape with c/a changed by $\approx 1.0\pm 5.8~{{\ \rm per\ cent}}$. Fractional changes of each of these properties depend primarily on the amount of mass in subhaloes and, to a lesser extent, mass accretion history. Our findings demonstrate that the properties of the smooth components of dark matter haloes are biased relative to the total halo mass.