Smoothing Factor Research Articles

Optimization of smoothing factor for fast non-local means algorithm in high pitch based low-dose computed tomography images with tin-filter

In this study, we applied the fast non-local means (FNLM) algorithm to high-pitch low-dose computed tomography (CT) images using a tin-filter. This study was conducted via a simulation study using a Shepp-Logan phantom and an experimental approach using American association of physicists in medicine (AAPM) phantom images. To optimize the smoothing factor of the FNLM algorithm, the values were adjusted from 0.01 to 0.05 at intervals of 0.001 and applied to the CT image. The smoothing factor of the optimal FNLM algorithm, derived through various quantitative evaluations, was 0.03. According to the results, when the optimized FNLM algorithm was applied to the acquired CT image with a low-dose protocol based on a high pitch condition, the estimated coefficient of variation, contrast-to-noise ratio, and signal-to-noise ratio were improved by 8.7, 6.7, and 8.0 times, respectively, compared to the general CT scanning condition (none). In addition, compared with the noise filtering methods mainly used in the past, both simulation and real experimental results proved that the application efficiency of the FNLM algorithm was high in the obtained low-dose-based CT images. In summary, the applicability of the FNLM algorithm in CT images obtained under low-dose conditions with high pitch using a tin-filter was demonstrated, and the derived research results are expected to be actively utilized in the diagnostic medical field to help reduce exposure doses and improve image quality.

Histogram Filter with Smoothing Parameter Setting

A histogram filter with smoothing parameter settings is discussed in the article. The histogram filter can be effectively applied in the problems of identification (recognition) of distribution laws for small amounts of data. The smoothing parameter is determined taking into account the available a priori information regarding the proposed distribution law. The relationship between the mathematical expectations of the chi-square fit criterion of the standard estimation histogram and the use of the histogram filter has been determined. This ratio is determined by the smoothing factor. The numerical value of the smoothing coefficient depends on the following parameters: the amount of data, the number of grouping intervals, and the shape parameters of the distribution law. The paper analyzes the feasibility of using a histogram filter, depending on the ratio of the above parameters. The dependence of the smoothing coefficient on the specified parameters allows one to determine the relationship between the number of data grouping intervals and their volume. The histogram filter is an easy-to-implement tool that can be easily integrated into any open distribution law identification (recognition) algorithm

Smooth factors of integers and elliptic curve based factoring with an oracle

Smooth factors of integers and elliptic curve based factoring with an oracle

PV Inverter Reliability Constrained Volt/Var Control With Power Smoothing via a Convex-Concave Programming Method

Intensive use of photovoltaic (PV) inverter in the volt/var control (VVC) methods in active distribution networks can impair inverter reliability. This article proposes a PV inverter reliability-constrained (PiReCon-) VVC method with a power smoothing scheme under uncertainties. First, considering impacts on inverter reliability, new reliability constraints are developed with a power smoothing factor to constrict inverter apparent power variation. Second, a new VVC optimization model is proposed with the reliability constraints, which minimizes power losses by optimizing both inverter var output and PV curtailment. Uncertainties of PV generation and loads are fully considered and addressed via a stochastic optimization method. Third, this article develops a penalty convex-concave programming method to effectively tackle nonconvexity of the proposed optimization model. The proposed PiReCon-VVC method is tested on a 33-bus distribution network, and simulation results verify its high efficiency in both minimizing the power losses and enhancing the PV inverter reliability.

A fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators.

In recent years, automatic fault diagnosis for various machines has been a hot topic in the industry. This paper focuses on permanent magnet synchronous generators and combines fuzzy decision theory with deep learning for this purpose. Thus, a fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators is proposed in this paper. The particle swarm algorithm optimizes the smoothing factor of the network for the effect of probabilistic neural network classification, as affected by the complexity of the structure and parameters. And on this basis, the fuzzy C means algorithm is used to obtain the clustering centers of the fault data, and the network model is reconstructed by selecting the samples closest to the clustering centers as the neurons in the probabilistic neural network. The mathematical analysis and derivation of the T-S (Tkagi-Sugneo) fuzzy neural network-based diagnosis strategy are carried out; the T-S fuzzy neural network-based generator fault diagnosis system is designed. The model is implemented on the MATLAB/Simulink platform for simulation and verification, the experiments show that the T-S fuzzy diagnosis strategy is significantly improved, and the design purpose is achieved. The fuzzy neural network has a parallel structure and can perform parallel data processing. This parallel mechanism can solve the problem of large-scale real-time computation in control systems, and the redundancy in parallel computation can make the control system highly fault-tolerant and robust. The fault diagnosis model based on an improved probabilistic neural network is applied to the fault data to verify the effectiveness and accuracy of the model.

Isosingular loci of algebraic varieties

We define the notion of isosingular loci of algebraic varieties, following the analytic case first studied by Ephraim. These are subsets of points where the variety has a prescribed formal singularity type. We show that the isosingular loci of an algebraic variety are locally closed in the Zariski topology and the associated reduced subschemes are smooth. Moreover, assuming characteristic 0, we prove the existence of a decomposition of the formal neighborhoods at closed points into a product of the respective isosingular locus at that point and a smooth factor. One of the main obstructions in the positive characteristic case is the non-separability of the orbit map associated to the contact group, as first observed by Greuel and Pham for isolated singularities.

Prevention of bleomycin-induced lung fibrosis via inhibition of the MRTF/SRF transcription pathway.

Bleomycin-induced lung fibrosis is a debilitating disease, linked to high morbidity and mortality in chemotherapy patients. The MRTF/SRF transcription pathway has been proposed as a potential therapeutic target, as it is critical for myofibroblast differentiation, a hallmark of fibrosis. In human lung fibroblasts, the MRTF/SRF pathway inhibitor, CCG-257081, effectively decreased mRNA levels of downstream genes: smooth muscle actin and connective tissue growth factor, with IC50 s of 4 and 15 μM, respectively. The ability of CCG-257081 to prevent inflammation and fibrosis, measured via pulmonary collagen content and histopathology, was tested in a murine model of bleomycin-induced lung fibrosis. Animals were given intraperitoneal bleomycin for 4 weeks and concurrently dosed with CCG-257081 (0, 10, 30, and 100 mg/kg PO), a clinical anti-fibrotic (nintedanib) or the clinical standard of care (prednisolone). Mice treated with 100 mg/kg CCG-257081 gained weight vs. vehicle-treated control mice, while those receiving nintedanib and prednisolone lost significant weight. Hydroxyproline content and histological findings in tissue of animals on 100 mg/kg CCG-257081 were not significantly different from naive tissue, indicating successful prevention. Measures of tissue fibrosis were comparable between CCG-257081 and nintedanib, but only the MRTF/SRF inhibitor decreased plasminogen activator inhibitor-1 (PAI-1), a marker linked to fibrosis, in bronchoalveolar lavage fluid. In contrast, prednisolone led to marked increases in lung fibrosis by all metrics. This study demonstrates the potential use of MRTF/SRF inhibitors to prevent bleomycin-induced lung fibrosis in a clinically relevant model of the disease.

MulOER-SAN: 2-layer multi-objective framework for exercise recommendation with self-attention networks

Diagnosing the knowledge mastery level of students on required concepts and providing tailored exercises to them is an essential task in e-learning environments. Due to the different knowledge level of students and the large scale of exercise banks, it is difficult for general recommenders to recommend suitable exercises for each student, which decreases their learning efficiency. In this paper, we develop a 2-layer multi-objective framework for exercise recommendation with self-attention networks, abbreviated as MulOER-SAN, to capture the change in students’ knowledge acquisition and thus provide customized exercise recommendations. Note that MulOER-SAN is a 2-layer framework. Via the bottom layer, a self-attention mechanism is adopted to predict the coverage in the next exercise, which can lead the learning process in the right direction. Moreover, we implement a novel knowledge tracing model with an enhanced self-attention sub-layer, thus tracing students’ dynamic knowledge state evolution. As the top layer of the 2-layer model, exercises are filtered according to the bottom layer’s predicted results, and the candidate subsets with appropriate difficulty and novelty are generated. From the perspective of diversity, we also carefully develop a chaotic sparrow search algorithm to further filter the candidate subset to avoid redundancy of recommended results. On the above basis, a simple yet effective difficulty smoothness factor is implemented to generate a high-quality ranking list in a real teaching application scenario. Comprehensive experiments conducted on three real-world datasets have demonstrated that the proposed MulOER-SAN framework significantly outperforms state-of-the-art methods in terms of various evaluation metrics.

A Framework for Generating Radial and Surface-Oriented Regularized Stokeslets.

Error in the method of regularized Stokeslets is highly dependent on the choice of the blob or regularization function that is utilized to handle singularities in the flow. In this work, we develop a general framework to choose regularizations at the level of the vector potential via smoothing factors. We detail the derivation for radial smoothing factors and specify properties which ensure that the solution is a regularized flow satisfying the incompressible Stokes equations. Error analysis is completed for both the far-field flow (away from the location of the forces) as well as at the location of the forces, relating our newly derived smoothing factors to commonly used blob functions and moment conditions. When forces are on a surface, we extend the radial smoothing factor case to the case of non-radial regularizations that are surface-oriented. We illustrate the utility of this framework by computing the forward and inverse problems of a translating sphere using radial and surface-oriented regularizations.

Novel mass-based multigrid relaxation schemes for the Stokes equations

In this work, we propose three novel block-structured multigrid relaxation schemes based on distributive relaxation, Braess–Sarazin relaxation, and Uzawa relaxation, for solving the Stokes equations discretized by the marker-and-cell scheme. The mass matrix obtained from the bilinear finite element method is directly used to approximate the inverse of scalar Laplacian operator in the relaxation schemes. Using local Fourier analysis, we theoretically derive optimal smoothing factors for the resulting three relaxation schemes. Specifically, mass-based distributive relaxation, mass-based Braess–Sarazin relaxation, and mass-based σ-Uzawa relaxation have optimal smoothing factor 13, 13 and 13, respectively. These smoothing factors are smaller than those in our earlier work [1], where weighted Jacobi iteration is used for inventing Laplacian involved in the Stokes equations. The mass-based relaxation schemes do not cost more than the original ones using the Jacobi iteration. Another superiority of mass-based relaxation is that there is no need to compute the inverse of a matrix. These new relaxation schemes are appealing.

A two-level optimization algorithm for path planning of bionic robotic fish in the three-dimensional environment with ocean currents and moving obstacles

Path planning is not only the premise of underwater navigation and obstacle avoidance, but also the core and key to improve the operation durability for the bionic robotic fish. In this paper, the path length, smoothness and safety factor are selected as the optimization performance indexes. In order to solve the path planning with ocean currents and moving obstacles, a two-level optimization algorithm is proposed for the bionic robotic fish. In the first level of optimization, it solves the global path planning problem with known environment. In the second level of optimization, based on the optimization results of the first level of optimization, the local optimal path is obtained by the dynamic optimization method in the environment with unknown static and moving obstacles, which can solve the problem of obstacle avoidance for irregular obstacles effectively. The two-level optimization algorithm improves the accuracy of the path planning. Fuzzy adaptive genetic algorithm (FAGA) is given to further improve the optimization performance. The proposed algorithm can improve the optimization performance of the path planning, whose effectiveness is demonstrated by the simulation and experiment results.

Improvements of the fifth-order WENO-JS-type scheme with normalized smoothing factor for gas dynamic Euler equations

In this paper, we developed a novel high-resolution fifth-order WENO-JS-type scheme that can achieve the optimal accuracy order at or near the arbitrary order critical points. To enhance the accuracy of the weight function of WENO-JS, we normalized the local smoothness factor to obtain a series of normalized variables. By introducing a mapping function that can increase the accuracy of the normalization variables, we developed a new normalized WENO-JS-type scheme called WENO-NSI. The WENO-NSI scheme can improve the shock capture ability by selecting the apposite parameter λ in the mapping function. Numerical experiments of one- and two-dimensional gas dynamic Euler equations for the present WENO-NSI scheme are compared with those of the WENO-JS/Z/M/D/IM and TENO-Z schemes. The results show that the present scheme can achieve theoretical accuracy at or near the arbitrary order critical points, and it has a better shock capturing ability than other schemes.

A real-time driver fatigue identification method based on GA-GRNN.

It is of great practical and theoretical significance to identify driver fatigue state in real time and accurately and provide active safety warning in time. In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. The specific work is as follows: (1) design simulated driving experiment and real driving experiment, determine the fatigue state of drivers according to the binary Karolinska Sleepiness Scale (KSS), and establish the fatigue driving sample database. (2) Improved Multi-Task Cascaded Convolutional Networks (MTCNN) and applied to face detection. Dlib library was used to extract the coordinate values of face feature points, collect the characteristic parameters of driver's eyes and mouth, and calculate the Euler Angle parameters of head posture. A fatigue identification model was constructed by using multiple characteristic parameters. (3) Genetic Algorithm (GA) was used to find the optimal smooth factor of Generalized Regression Neural Network (GRNN) and construct GA-GRNN fatigue driving identification model. Compared with K-Nearest Neighbor (KNN), Random Forest (RF), and GRNN fatigue driving identification algorithms. GA-GRNN has the best generalization ability and high stability, with an accuracy of 93.3%. This study provides theoretical and technical support for the application of driver fatigue identification.

A gas–solid flow pattern identification algorithm based on cross-rod electrostatic sensor array

The accurate identification of gas–solid two-phase flow patterns is an important but challenging subject for pneumatic conveying. In this study, the sensitivity deficiencies of a single electrode were analysed via finite element analysis and a more sensitive cross-rod electrostatic sensor array structure was designed to measure the flow pattern signals. The experiment used Geldart D particles to verify the feasibility of the designed sensor array. Three types of feature vectors were extracted: the mean value, variance, and energy ratio. To identify the flow pattern accurately, the sine–cosine algorithm (SCA) is exploited to optimise the smoothing factor critical for a probabilistic neural network (PNN), namely SCA-PNN. The identification results show that the identification accuracy of the proposed algorithm outperforms the traditional PNN, the back propagation neural network (BPNN) and the support vector machine (SVM).

Optimized sparse approximate inverse smoothers for solving Laplacian linear systems

In this paper we propose and analyze new efficient sparse approximate inverse (SAI) smoothers for solving the two-dimensional (2D) and three-dimensional (3D) Laplacian linear system with geometric multigrid methods. Local Fourier analysis shows that our proposed SAI smoother for 2D achieves a much smaller smoothing factor than the state-of-the-art SAI smoother studied in Bolten et al. (2016) [12]. The proposed SAI smoother for 3D cases provides smaller optimal smoothing factor than that of weighted Jacobi smoother. Numerical results validate our theoretical conclusions and illustrate the high-efficiency and high-effectiveness of our proposed SAI smoothers. Such SAI smoothers have the advantage of inherent parallelism.

Rotating Single-Antenna Spoofing Signal Detection Method Based on IPNN.

The traditional carrier-phase differential detection technology mainly relies on the spatial processing method, which uses antenna arrays or moving antennas to detect spoofing signals, but it cannot be applied to static single-antenna receivers. Aiming at this problem, this paper proposes a rotating single-antenna spoofing signal detection method based on the improved probabilistic neural network (IPNN). When the receiver antenna rotates at a constant speed, the carrier-phase double difference of the real signal will change with the incident angle of the satellite. According to this feature, the classification and detection of spoofing signals can be realized. Firstly, the rotating single-antenna receiver collects carrier-phase values and performs double-difference processing. Then, we construct an IPNN model, whose smoothing factor can be adaptively adjusted according to the type of failure mode. Finally, we use the IPNN model to realize the classification and processing of the carrier-phase double-difference observations and obtain the deception detection results. In addition, in order to reflect that the method has a certain practical value, we simulate the spoofing scenario of satellite signals and effectively identify abnormal satellite signals according to the detection results of the inter-satellite differential combination. Actual experiments indicate that the detection accuracy of the proposed method for spoofing signals reaches 98.84%, which is significantly better than the classical probabilistic neural network (PNN) and back-propagation neural network (BPNN), and the method can be implemented in fixed base station receivers for the real-time detection of forwarding spoofing.

Human lymph node degeneration in the thoracic region: A morphometric and immunohistochemical analysis using surgically obtained specimens.

Lymph node degeneration was examined in 539 mediastinal and intrapulmonary nodes removed from 78 patients, aged 49–82 years, without cancer metastasis. Medullary sinus hyalinization observed in 36.2% of the hilar and 38.5% of the interlobar nodes. Early and smaller lesions were eosinophilic and factor VIII-positive, whereas advanced and large lesions contained a bulky mass of collagenous fiber bundles with few slender cells positive for smooth muscle actin (SMA) and factor VIII, as well as anthracotic macrophages. Subcapsular sinus hyalinization, observed in 4.3% of hilar nodes, was detected as a thick fibrous layer (over 0.2 mm) between the surface cortex and the thickened capsule. The fibrous layer contained SMA-positive slender cells, whereas the thickened capsule contained fibers positive for elastin and factor VIII. These hyalinization lesions occupied 3.6% and 0.8% of the sectional areas of hilar and lower paratracheal nodes, respectively. Areas of early and small cortical degeneration, surrounded by fibers positive for SMA and vimentin, did not contain lymphocytes and macrophages, but contained abundant small stromal cells. Silver staining suggested that advanced cortical degeneration was composed of collagen fibrils other than type I. Fatty tissues, seen in 47.8% of hilar nodes, often extended into and replaced medullary sinus tissue. Island-like remnants of medullary sinuses in areas of fatty degeneration contained various stromal cells positive for SMA, elastin, factor VIII and/or CD34. These degenerative morphologies, however, did not correlate with either age or smoking index. The present cortical degeneration usually seemed to follow hyalinization, but both were likely to occur independently.

A Personalized Recommendation System for English Teaching Resources Based on Learning Behavior Detection

When confronted with a plethora of resources, many students struggle to quickly filter out the content that is relevant to them. Because there are many English teaching resources and it is difficult to accurately recommend suitable teaching resources for students. Therefore, in this paper we suggest a personalized recommendation system for English teaching resources, which is founded on learning behavior detection. To begin with, a spatiotemporal convolutional network is introduced to effectively identify students’ online classroom behavior, and a global attention module is added to increase the model’s ability to learn global feature information. Furthermore, the identified characteristics of student behavior are incorporated into the recommendation module. Similarly, the differential evolution (DE) algorithm is implemented to the smoothing factor and kernel function center of a generalized regression neural network (CRNN) for resource recommendation mode, while taking into account the strong dependence of the GRNN training effect on the smoothing factor and the kernel function center. The smoothing factor and offset factor are optimized and solved, and the optimized smoothing factor and offset factor are used to recommend GRNN resources. Experiments show that the approach described in this work first has a high precision (i.e., 90.98%) in behavior recognition, and second, the recommendation performance is superior to both of the comparison algorithms (i.e., 85.23% and 78.33%), resulting in better resource recommendation accuracy. The fundamental goal of this work is to deliver several important guidelines for the informatization and intelligence of the English educational resources and services.

Dihydroquercetin (DHQ) has the potential to promote apoptosis in ovarian cancer cells: An in silico and in vitro study

Ovarian cancer is the third leading gynaecological malignancy that leads to death in females annually. According to the GLOBOCAN 2020 incidence cases in Asia, there were a total of 100,854 ovarian cancer incidence cases. The Food and Drug Administration (FDA) has approved some drug compounds from both the generic and pharmaceutical brands for ovarian cancer, however, these synthetic compounds have many adverse effects such as – infections, fatigue, nerve problems, alopecia (hair loss), weight loss, lack of appetite, chemobrain (forgetfulness). By exploring both the in silico and in vitro aspects, we suggest that dihydroquercetin (DHQ) or simply taxifolin is an effective herbal compound that is druggable, leadlike, has good bioavailability, medicinal chemistry with a higher solubility with no adverse side effects, and toxicity. It also showcased a greater and more stable binding affinity and energy landscape with target POTEE with minimal energy frustrations and clashes. Our study also was able to predict a positive classification model of DHQ with a Tanimoto-distance threshold (d0 = 0.8) and smoothing factor (h = 0.9). We observed that the accuracy decreases to 90% from 100% at distance = 0.7, predicted number of compounds rose from 80 to 95%, which can be worth the loss of accuracy. Furthermore, our in vitro analysis on SK-OV-3 cell lines showcased that the anticancerous activity of DHQ caused significant cancer cell death and cell proliferation at 100 µm and 250 µm solutions. The DHQ drug dosages were further verified by Calcein-AM-PI live dead cell imaging on both concentrations. We observed that at 250 µm, DHQ caused apoptosis in SK-OV-3 cells. Hence, it is concluded that DHQ reduces ovarian cell proliferation and eventually cell death (apoptosis) in ovarian cancer.

An Approach for Enhancement of MR Images of Brain Tumor

Magnetic Resonance Imaging plays an important role in diagnosing the brain tumor accurately, but it requires the approach to enhance the magnetic resonance images to assist physicians in brain tumor detection and making the treatment plan precisely to reduce the mortality rate. Therefore, in this proposed work, a comprehensive learning-based elephant herding optimization technique has been introduced to select the optimal value of smoothness factor in Bi-Histogram Equalization with Adaptive Sigmoid Function that enhances the visual quality as well as the appearance of the suspicious regions in magnetic resonance images. Further, the enhancement performance has been evaluated by the enhancement quality metrics. The metrics used include mean square error, peak signal to noise ratio, mean absolute error, structural similarity index metric, feature similarity index metric, Riesz transformed based feature similarity index metric, spectral residual-based similarity index metric, and absolute mean brightness error. The outcomes of this proposed work have a remarkable impact on enhancing magnetic resonance images and providing visual assistance for diagnosing brain tumors. The performance of the evaluation metrics is verified with Friedman's mean rank test, which strongly indicates a statistical difference between the proposed method and state-of-the-art methods.