Effects of combining multiple dose reduction techniques on coronary computed tomography angiography

BACKGROUND Coronary computed tomography angiography(CCTA)is the preferred noninvasive examination method for coronary heart disease.However,the radiation from computed tomography has become a concern since public awareness of radiation hazards continue to increase.AIM To explore the value of multiple dose reduction techniques for CCTA.METHODS Consecutive normal and overweight patients were prospectively divided into two groups:Group A1,patients who received multiple dose reduction scans(n=82);and group A2,patients who received conventional scans(n=39).The scan parameters for group A1 were as follows:Isocentric scan,tube voltage=80 kV,and tube current control using 80%smart milliampere.The scan parameters for group A2 were as follows:Normal position,tube voltage=100 kV,and smart milliampere.RESULTS The average effective doses(EDs)for groups A1 and A2 were 1.13±0.35 and 3.36±1.30 mSv,respectively.There was a statistically significant difference in ED between the two groups(P<0.01).Furthermore,noise was significantly lower,and both signal-to-noise ratio and contrast signal-to-noise ratio were higher in group A2 when compared to group A1(P<0.01).Moreover,the subjective image quality(IQ)scores were excellent in both groups,in which there was no significant difference in subjective IQ score between the two groups(P=0.12).CONCLUSION Multiple dose reduction scan techniques can significantly decrease the ED of patients receiving CCTA examinations for clinical diagnosis.

Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

Association rules’learning is a machine learning method used in finding underlying associations in large datasets.Whether intentionally or unintentionally present,noise in training instances causes overfitting while building the classifier and negatively impacts classification accuracy.This paper uses instance reduction techniques for the datasets before mining the association rules and building the classifier.Instance reduction techniques were originally developed to reduce memory requirements in instance-based learning.This paper utilizes them to remove noise from the dataset before training the association rules classifier.Extensive experiments were conducted to assess the accuracy of association rules with different instance reduction techniques,namely:DecrementalReduction Optimization Procedure(DROP)3,DROP5,ALL K-Nearest Neighbors(ALLKNN),Edited Nearest Neighbor(ENN),and Repeated Edited Nearest Neighbor(RENN)in different noise ratios.Experiments show that instance reduction techniques substantially improved the average classification accuracy on three different noise levels:0%,5%,and 10%.The RENN algorithm achieved the highest levels of accuracy with a significant improvement on seven out of eight used datasets from the University of California Irvine(UCI)machine learning repository.The improvements were more apparent in the 5%and the 10%noise cases.When RENN was applied,the average classification accuracy for the eight datasets in the zero-noise test enhanced from 70.47%to 76.65%compared to the original test.The average accuracy was improved from 66.08%to 77.47%for the 5%-noise case and from 59.89%to 77.59%in the 10%-noise case.Higher confidence was also reported in building the association rules when RENN was used.The above results indicate that RENN is a good solution in removing noise and avoiding overfitting during the construction of the association rules classifier,especially in noisy domains.

Impact of Continuous Cropping on Soil Phenolic Acid Substances and Research Progress on Continuous Cropping Obstacle Reduction Techniques

At present,long-term continuous cropping in agricultural production has formed a relatively common development trend.With the increase of continuous cropping years,soil phenolic acids are also affected to varying degrees.This paper summarized the effects of continuous cropping on soil phenolic acids and the research progress of continuous cropping obstacle reduction techniques,aiming at providing theoretical basis and technical support for the research of continuous cropping obstacle reduction techniques and promoting the healthy and sustainable development of modern agriculture.

Review of Dimension Reduction Methods

Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR techniques were explored. The different data types that have been applied on the various DR techniques were also explored.

Satellite Data Reduction Using Entropy-preserved Image Compression Technique

In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is. given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.

A regional suspended load yield estimation model for ungauged watersheds

Developing regional models using physiographic, climatic, and hydrologic variables is an approach to estimating suspended load yield(SLY)in ungauged watersheds. However, using all the variables might reduce the applicability of these models. Therefore, data reduction techniques(DRTs), e.g., principal component analysis(PCA), Gamma test(GT), and stepwise regression(SR), have been used to select the most effective variables. The artificial neural network(ANN) and multiple linear regression(MLR) are also common tools for SLY modeling. We conducted this study(1) to obtain the most effective variables influencing SLY through DRTs including PCA, GT, and SR, and then, to use them as input data for ANN and MLR; and(2) to provide the best SLY models. Accordingly, we used 14 physiographic, climatic, and hydrologic parameters from 42 watersheds in the Hyrcanian forest region(in northern Iran). The most effective variables as determined through DRTs as well as the original data sets were used as the input data for ANN and MLR in order to provide an SLY model. The results indicated that the SLY models provided by ANN performed much better than the MLR models, and the GT-ANN model was the best. The determination of coefficient,relative error, root mean square error, and bias were 99.9%, 26%, 323 t/year, and 6 t/year in the calibration period, and 70%, 43%, 456 t/year, and 407 t/year in the validation period, respectively. Overall, selecting the main factors that influence SLY and using artificial intelligence tools can be useful for water resources managers to quickly determine the behavior of SLY in ungauged watersheds.

Finite element analyses of slope stability problems using non-associated plasticity

In recent years, finite element analyses have increasingly been utilized for slope stability problems. In comparison to limit equilibrium methods, numerical analyses do not require any definition of the failure mechanism a priori and enable the determination of the safety level more accurately. The paper compares the performances of strength reduction finite element analysis(SRFEA) with finite element limit analysis(FELA), whereby the focus is related to non-associated plasticity. Displacement-based finite element analyses using a strength reduction technique suffer from numerical instabilities when using non-associated plasticity, especially when dealing with high friction angles but moderate dilatancy angles. The FELA on the other hand provides rigorous upper and lower bounds of the factor of safety(FoS) but is restricted to associated flow rules. Suggestions to overcome this problem, proposed by Davis(1968), lead to conservative FoSs; therefore, an enhanced procedure has been investigated. When using the modified approach, both the SRFEA and the FELA provide very similar results. Further studies highlight the advantages of using an adaptive mesh refinement to determine FoSs. Additionally, it is shown that the initial stress field does not affect the FoS when using a Mohr-Coulomb failure criterion.

Data release of the LAMOST pilot survey

This paper describes the data release of the LAMOST pilot survey, which includes data reduction, calibration, spectral analysis, data products and data access. The accuracy of the released data and the information about the FITS headers of spectra are also introduced. The released data set includes 319 000 spectra and a catalog of these objects.

South Galactic Cap u-band Sky Survey(SCUSS):Project Overview

The South Galactic Cap u-band Sky Survey （SCUSS） was established in 2009 in order to provide a photometric input catalog for target selection of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） project. SCUSS is an international cooperative project between National Astronomical Observatories, Chinese Academy of Sciences, and Steward Observatory at the University of Arizona, using the 90 inch （2.3 m） Bok telescope on Kitt Peak. The telescope is equipped with a prime focus camera that is composed of a mosaic of four 4096 × 4096 CCDs and has a field of view of about 1 deg2. From 2009 to 2013, SCUSS performed a sky survey of an approximately 5000 deg2 field of the South Galactic Cap in u band, including the Galactic anticenter area and the SDSS-IV extended imaging area. The limiting magnitude of SCUSS is deeper than 23 mag （at a signal-to-noise ratio of 5）. In this paper, we briefly describe the goals of this project, method of observations and data reduction, and we also introduce current and potential scientific activities related to the SCUSS project.

Monte Carlo Method and Its Variance Reduction Techniques for On-stream Coal Slurry Parameter Determination

The special purpose Monte Carlo program McMesh was used to study neutron transport in coal slurries for on stream determination of the slurry parameters. McMesh uses the mesh weight window method as the major variance reduction technique with other methods such as exponential transforms and correlated sampling included as options. There was good agreement between the calculated results from McMesh and from MCNP, a general Monte Carlo program, but McMesh was more efficient and more convenient.

Numerical study of anomalous dynamic scaling behaviour of (1+1)-dimensional Das Sarma-Tamborenea model

In order to discuss the finite-size effect and the anomalous dynamic scaling behaviour of Das Sarma-Tamborenea growth model, the （1＋1）-dimensional Das Sarma-Tamborenea model is simulated on a large length scale by using the kinetic Monte-Carlo method. In the simulation, noise reduction technique is used in order to eliminate the crossover effect. Our results show that due to the existence of the finite-size effect, the effective global roughness exponent of the （1＋1）-dimensional Das Sarma-Tamborenea model systematically decreases with system size L increasing when L 〉 256. This finding proves the conjecture by Aarao Reis[Aarao Reis F D A 2004 Phys. Rev. E 70 031607]. In addition, our simulation results also show that the Das Sarma-Tamborenea model in 1＋1 dimensions indeed exhibits intrinsic anomalous scaling behaviour.

Model Reduction and Controller Design for a Nonlinear Heat Conduction Problem Using Finite Element Method

The mathematical models for dynamic distributed parameter systems are given by systems of partial differential equations. With nonlinear material properties, the corresponding finite element (FE) models are large systems of nonlinear ordinary differential equations. However, in most cases, the actual dynamics of interest involve only a few of the variables, for which model reduction strategies based on system theoretical concepts can be immensely useful. This paper considers the problem of controlling a three dimensional profile on nontrivial geometries. Dynamic model is obtained by discretizing the domain using FE method. A nonlinear control law is proposed which transfers any arbitrary initial temperature profile to another arbitrary desired one. The large dynamic model is reduced using proper orthogonal decomposition (POD). Finally, the stability of the control law is proved through Lyapunov analysis. Results of numerical implementation are presented and possible further extensions are identified.

Measurement of the echo reduction for underwater acoustic passive materials by using the time reversal technique

A measuring method of the echo reduction of passive materials by using the time reversal（TR） technique is presented. To measure the echo reduction of a sample with this approach, the received signals are firstly focused according to the TR theory. Then, the sample is removed and the TR processing is again employed to realize the focus of the received signal.Finally, the echo reduction of the sample is evaluated with these focusing signals. Besides, to calibrate the measured echo reduction via the TR technique, a standard sample is employed to measure a constant coefficient that only depends on the measurement environment. An aluminum plate sample and a steel plate sample with the same size of 1.1 mxl.O m x0.005 m axe tested in a wave guide tank. The experimental results show that the calibrated values are well consistent with theoretical results under the free field at the measured frequency range of0.5-20 kHz. The relative errors of all the measured values are less than 10% and the values of the expanded uncertainty are less than 1.5 dB. The TR processing focuses the energy in spatial domain and temporal domain, so it can be used to measure the echo reduction of passive materials in the environments with reflections induced by boundaries and low frequency sources.

A Brief Review on Application of Information Technique to Agrometeorological Disaster Reduction

Disaster reduction strategy of agrometeorological disasters is a very complicated systematic project. The system is divided into two subsystems: agrometeorological disaster subsystem and dis aster reduction strategy subsystem. The agrometeorological disaster subsystem incIudes the whole process of disaster from occurrence, development to extinction. The subsystem of disaster reduction strategy is strategy measures included prevention, resistance and remedy of disaster The system involves monitoring, forecasting, prevention, resistance, control, loss assessment of disaster and efficiency analysis of disaster reduction. Therefore, modern information techniques (remote sensing, artificial intelligence, crop simulation etc. ) are necessary for improving the a bility of prevention and reduction of