Plaque rupture and subsequent thrombosis are major processes of acute cardiovascular events. The Vulnerability Index is a very important indicator of whether a plaque is ruptured, and these easily ...ruptured or fragile plaques can be detected early. The higher the general vulnerability index, the higher the instability of the plaque. Therefore, determining a clear vulnerability index classification point can effectively reduce unnecessary interventional therapy. However, the current critical value of the vulnerability index has not been well defined. In this study, we proposed a neural network-based method to determine the critical point of vulnerability index that distinguishes vulnerable plaques from stable ones. Firstly, based on MatConvNet, the intravascular ultrasound images under different vulnerability index labels are classified. Different vulnerability indexes can obtain different accuracy rates for the demarcation points. The corresponding data points are fitted to find the existing relationship to judge the highest classification. In this way, the vulnerability index corresponding to the highest classification accuracy rate is judged. Then the article is based on the same experiment of different components of the aortic artery in the artificial neural network, and finally the vulnerability index corresponding to the highest classification accuracy can be obtained. The results show that the best vulnerability index point is 1.716 when the experiment is based on the intravascular ultrasound image, and the best vulnerability index point is 1.607 when the experiment is based on the aortic artery component data. Moreover, the vulnerability index and classification accuracy rate has a periodic relationship within a certain range, and finally the highest AUC is 0.7143 based on the obtained vulnerability index point on the verification set. In this paper, the convolution neural network is used to find the best vulnerability index classification points. The experimental results show that this method has the guiding significance for the classification and diagnosis of vulnerable plaques, further reduce interventional treatment of cardiovascular disease.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Using the parameterized susceptible‐exposed‐infectious‐recovered model, we simulated the spread dynamics of coronavirus disease 2019 (COVID‐19) outbreak and impact of different control measures, ...conducted the sensitivity analysis to identify the key factor, plotted the trend curve of effective reproductive number (R), and performed data fitting after the simulation. By simulation and data fitting, the model showed the peak existing confirmed cases of 59 769 arriving on 15 February 2020, with the coefficient of determination close to 1 and the fitting bias 3.02%, suggesting high precision of the data‐fitting results. More rigorous government control policies were associated with a slower increase in the infected population. Isolation and protective procedures would be less effective as more cases accrue, so the optimization of the treatment plan and the development of specific drugs would be of more importance. There was an upward trend of R in the beginning, followed by a downward trend, a temporary rebound, and another continuous decline. The feature of high infectiousness for severe acute respiratory syndrome coronavirus 2(SARS‐CoV‐2) led to an upward trend, and government measures contributed to the temporary rebound and declines. The declines of R could be exploited as strong evidence for the effectiveness of the interventions. Evidence from the four‐phase stringent measures showed that it was significant to ensure early detection, early isolation, early treatment, adequate medical supplies, patients’ being admitted to designated hospitals, and comprehensive therapeutic strategy. Collaborative efforts are required to combat the novel coronavirus, focusing on both persistent strict domestic interventions and vigilance against exogenous imported cases.
Research Highlights
The SEIR model showed the peak existing confirmed cases of 59769 arriving on 15 February 2020, with high precision of the data fitting results. The dynamic changes of R values were taken into account on different scenarios of the epidemic. The declines of R indicated the effectiveness of the four‐phase government interventions. Given the situation of pandemic potential, the stringent measures by Chinese government could be referential and enlightening to the other countries suffering from COVID‐19.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Filled function method, as an efficient method for finding the global minimizer of multimodal functions. It uses alternately classical optimization methods to minimize the objective function and the ...filled function until the global minimizer of the objective function is obtained. However, many efficient optimization methods based on gradient information cannot be used to minimize the filled function, which is not continuously differentiable. In addition, filled functions often contain parameters that are difficult to adjust. In this paper, a parameter-free continuously differentiable filled function without exponential terms or logarithmic terms and the corresponding filled function algorithm are proposed to overcome these shortcomings. Theoretical analysis and numerical verification show that our algorithm is feasible and effective. Furthermore, the algorithm is applied to solve nonlinear equations and data fitting problems. The numerical results prove the effectiveness of our algorithm. Finally, the advantages and disadvantages of our algorithm are analyzed, and the further research direction is discussed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Ni 2p/La 3d signal overlapping can be solved by using Shirley Background.•Ni 2p signal is strongly overlapped by La 3d in high La content FCC catalyst.•Fe 2 s signal can be neglected in normal Fe ...content FCC catalyst.
Herein, a methodology to study the content of nickel and lanthanum by X-Ray photoelectron spectroscopy - XPS in FCC catalyst was developed. A critical problem to study the nickel in this type of material is related to the overlapping between Ni 2p3/2 and La 3d3/2 signals. In order to have an accurate and correct interpretation of XPS spectra, La 3d and Ni 2p signals were studied individually employing synthesized and FCC catalyst samples. Fitting parameters such as: background modeling method and fitting constraints were determined and tested in synthesized and FCC catalysts samples. Finally, the XPS fitting method obtained was employed to study the La 3d/Ni 2p region in FCC catalyst samples, showing a good performance to study this region.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
As researchers focus on the field of quantum machine learning, there are more and more quantum algorithms for data fitting. However, the majority of quantum algorithms for data fitting are only ...applicable to sparse matrices, which bring a large number of limitations to their application. In this paper, to solve this problem, we propose a quantum algorithm for data fitting for general matrices, not only sparse and dense matrices. The quantum algorithm is mainly composed of three subroutines which are generating target state, quantum state tomography and estimating fit quality. The time complexity of the quantum algorithm is Oκ4‖A‖Fpolylog(MN)(M′2log(M′)2/ɛ3+1/(ɛϵ2)), where A∈RM×N, κ is the conditional number of matrix A†A∈RN×N, ||A||F is the Frobenius norm of A, M′ is the maxima number of fit functions in allowing fit, ɛ is the fitting error, and ϵ is the error of fit quality estimation. Moreover, our quantum algorithm is independent of the sparsity of the matrix, so our algorithm has wide application in dense matrices and poses a new state of the art for solving dense data fitting.
•Data fitting is a mathematical method to construct the least squares fitting problem.•The proposed quantum algorithm is independent of the sparsity of the matrix.•The algorithm achieves polynomial-scale speedup compared to other algorithms.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Separable nonlinear least-squares (SNLLS) problems arise frequently in many research fields, such as system identification and machine learning. The variable projection (VP) method is a very powerful ...tool for solving such problems. In this paper, we consider the regularization of ill-conditioned SNLLS problems based on the VP method. Selecting an appropriate regularization parameter is difficult because of the nonlinear optimization procedure. We propose to determine the regularization parameter using the weighted generalized cross-validation method at every iteration. This makes the original objective function changing during the optimization procedure. To circumvent this problem, we use an inequation to produce a consistent demand of decreasing at successive iterations. The approximation of the Jacobian of the regularized problem is also discussed. The proposed regularized VP algorithm is tested by the parameter estimation problem of several statistical models. Numerical results demonstrate the effectiveness of the proposed algorithm.
•We proposed a compartmental model with both human and rodent population to study the Lassa fever epidemics.•The model exhibits the phenomena of forward bifurcation that the stability between the ...disease-free equilibrium and the endemic equilibrium exchanges.•The model fits the Lassa fever epidemics from 2016-19 with same reconstructed rodent-borne transmission rate.•The estimated initial susceptibility increased across the epidemics from 2016 to 2019, which indicates the increase in the case prevalence rate.
Lassa fever, also known as Lassa hemorrhagic fever, is a virus that has generated recurrent outbreaks in West Africa. We use mechanistic modelling to study the Lassa fever epidemics in Nigeria from 2016-19. Our model describes the interaction between human and rodent populations with the consideration of quarantine, isolation and hospitalization processes. Our model supports the phenomenon of forward bifurcation where the stability between disease-free equilibrium and endemic equilibrium exchanges. Moreover, our model captures well the incidence curves from surveillance data. In particular, our model is able to reconstruct the periodic rodent and human forces of infection. Furthermore, we suggest that the three major epidemics from 2016-19 can be modelled by properly characterizing the rodent (or human) force of infection while the estimated human force of infection also present similar patterns across outbreaks. Our results suggest that the initial susceptibility likely increased across the three outbreaks from 2016-19. Our results highlight the similarity of the transmission dynamics driving three major Lassa fever outbreaks in the endemic areas.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
For large-scale data fitting, the least-squares progressive iterative approximation is a widely used method in many applied domains because of its intuitive geometric meaning and efficiency. In this ...work, we present a randomized progressive iterative approximation (RPIA) for the B-spline curve and surface fittings. In each iteration, RPIA locally adjusts the control points according to a random criterion of index selections. The difference for each control point is computed concerning the randomized block coordinate descent method. From geometric and algebraic aspects, the illustrations of RPIA are provided. We prove that RPIA constructs a series of fitting curves (resp., surfaces), whose limit curve (resp., surface) can converge in expectation to the least-squares fitting result of the given data points. Numerical experiments are given to confirm our results and show the benefits of RPIA.
•Randomization techniques. Introducing randomization strategies into data fitting methods, such methods are easy to operate and have straightforward theoretical analysis.•The local least squares solver. Our method is an approximate PIA algorithm, which only locally updates the control points concerning an index set.•Computing complexity. Different from the traditional PIA surface fitting methods, our method does not need to operate the Kronecker product of two collocation matrices.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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