The Prevalence of Congenital Heart Disease among School-Age Children in China:A Meta-Analysis and Systematic Review

Objectives:To estimate the prevalence of Congenital Heart Disease(CHD)in school-age children,to identify the extent to which altitude affects the prevalence of the disease,and to examine trends in prevalence over time in China.Methods:Seven databases were systematically searched and last retrieved on September 10,2021 for all studies reporting the prevalence of CHD in children after 1970 in China,which were then divided into high and low altitude regions based on 2500 meters above sea level.The random-effected model was used to combine prevalence data and subgroups analysis.The baseline data of all cases and individuals were used for comparison to calculate the odds ratio(OR)for overall and different altitude prevalence.Results:A total of 12,926,083 individuals(aged 3-18 years),with 31,835 cases from 86 studies,were included in the analysis.The pooled CHD prevalence of total children was 4.69[95%confidence interval(CI):4.10 to 5.29]per 1000 children.Overall,temporal trends analysis indicated that the prevalence of CHD in children continuously decreased with time,from 6.19(95%CI:4.50 to 7.88)per 1000 children in 1976-1985 to 3.30(95%CI:2.49;4.38)per 1000 children in 2016-2021.The OR for the prevalence of CHD in children from high and low altitudes with baseline data was 2.84(95%CI:2.48 to 3.27)and 1.31(95%CI:1.13 to 1.53)(χ^(2)=53.89,p<0.01),respectively.The OR of the prevalence of CHD in male children compared to females was 0.60(95%CI:0.53 to 0.68)at high altitudes and 0.79(95%CI:0.71 to 0.89)at low altitudes.Among the seven most common subtypes,patent ductus arteriosus was the most common at high altitudes,while atrial septal defects were the most common at low altitudes.Conclusion:This study provides valuable insights for further disease prevention and etiological exploration.The overall decreasing trend in the prevalence of CHD in children over time may indicate a positive effect of perinatal management and treatment during infancy.

Two-proton radioactivity from excited states of proton-rich nuclei within Coulomb and Proximity Potential Model

In the present work,we extend the Coulomb and Proximity Potential Model(CPPM)to study two-proton(2p)radioactivity from excited states while the proximity potential is chosen as AW95 proposed by Aage Withner in 1995.Demonstration reveals that the theoretical results acquired by CPPM exhibit a high level of consistency with prior theoretical models such as the unified fission model(UFM),generalized liquid-drop model(GLDM)and effective liquid-drop model(ELDM).Furthermore,within the CPPM,we predicted the half-lives of potential 2p radioactive nuclei for which experimental data are currently unavailable.The predicted results were then assessed,compared with UFM,ELDM and GLDM models,and examined in detail.

COVID-19 Imaging Detection in the Context of Artificial Intelligence and the Internet of Things

Coronavirus disease 2019 brings a huge burden on the medical industry all over the world.In the background of artificial intelligence(AI)and Internet of Things(IoT)technologies,chest computed tomography(CT)and chest Xray(CXR)scans are becoming more intelligent,and playing an increasingly vital role in the diagnosis and treatment of diseases.This paper will introduce the segmentation of methods and applications.CXR and CT diagnosis of COVID-19 based on deep learning,which can be widely used to fight against COVID-19.

Two-proton radioactivity of the excited state within the Gamow-like and modified Gamow-like models

In this study,we systematically investigated the two-proton(2p)radioactivity half-lives from the excited state of nuclei near the proton drip line within the Gamowlike model(GLM)and modified Gamow-like model(MGLM).The calculated results were highly consistent with the theoretical values obtained using the unified fission model[Chin.Phys.C 45,124105(2021)],effective liquid drop model,and generalized liquid drop model[Acta Phys.Sin 71,062301(2022)].Furthermore,utilizing the GLM and MGLM,we predicted the 2p radioactivity halflives from the excited state for some nuclei that are not yet available experimentally.Simultaneously,by analyzing the calculated results from these theoretical models,it was found that the half-lives are strongly dependent on Qand l.

Systematic study of proton radioactivity half‑lives

In the present study,on the basis of the screened electrostatic effect of the Coulomb potential,we propose an improved Gamow model within the centrifugal potential in which there are only two adjustable parameters,i.e.,the screened parameters t and g,which represent the combined effect of the interaction potential and reduced mass of the emitted proton-daughter nucleus on the half-life of proton radioactivity in the overlapping region.Using this model,we systematically calculated the proton radioactivity half-lives of 31 spherical nuclei and 13 deformed nuclei and obtained corresponding root-mean-square deviations of 0.274 and 0.367,respectively.The relationship between the proton radioactivity half-life of 177Tlm and the corresponding angular momentum l removed by the emitted proton is also discussed.In addition,we used the proposed model to predict the proton radioactivity half-lives of 18 nuclei whose proton radioactivity is energetically allowed or observed but not yet quantified in NUBASE2020.For comparison,we used the universal decay law of proton radioactivity proposed by Qi et al.(Phys Rev C 85:011303,2012.https://doi.org/10.1103/PhysR evC.85.011303),and the new Geiger–Nuttall law of proton radioactivity proposed by Chen et al.(Eur Phys J 55:214,2019.https://doi.org/10.1140/epja/i2019-12927-7).

Iterative convergence theorems for maximal monotone operators and relatively nonexpansive mappings

In this paper, some iterative schemes for approximating the common element of the set of zero points of maximal monotone operators and the set of fixed points of relatively nonexpansive mappings in a real uniformly smooth and uniformly convex Banach space are proposed. Some strong convergence theorems are obtained, to extend the previous work.

On the Growth of Solutions of a Class of Higher Order Linear Differential Equations with Meromorphic Coefficients

In this paper, we investigate the growth of solutions of higher order linear differential equations with meromorphic coefficients. Under certain conditions, we obtain precise estimation of growth order and hyper-order of solutions of the equation.

An FEM approximation for a fourth-order variational inequality of second kind

A fourth-order variational inequality of the second kind arising in a plate frictional bending problem is considered. By using regularization method, the original problem can be formulated as a differentiable variational equation, and the corresponding discrete FEM variational equation is presented afterwards. Abstract error estimates and error estimates of the approximation are derived in terms of energy norm and L^2-norm.

Finite Volume Element Predictor-corrector Method for a Class of Nonlinear Parabolic Systems

A finite volume element predictor-corrector method for a class of nonlinear parabolic system of equations is presented and analyzed. Suboptimal L^2 error estimate for the finite volume element predictor-corrector method is derived. A numerical experiment shows that the numerical results are consistent with theoretical analysis.

Robustness of iterative learning control for a class offractional-order linear continuous-time switched systems in the sense of L^p norm

For a class of fractional-order linear continuous-time switched systems specified by an arbitrary switching sequence,the performance of PDα-type fractional-order iterative learning control(FOILC)is discussed in the sense of L^p norm.When the systems are disturbed by bounded external noises,robustness of the PDα-type algorithm is firstly analyzed in the iteration domain by taking advantage of the generalized Young inequality of convolution integral.Then,convergence of the algorithm is discussed for the systems without any external noise.The results demonstrate that,under some given conditions,both convergence and robustness can be guaranteed during the entire time interval.Simulations support the correctness of the theory.

PERTURBATION RESULTS OF SECOND ORDER LINEAR DIFFERENTIAL EQUATIONS WITH PERIODIC COEFFICIENTS

Two perturbation results on the linear differential function f″ ＋ Π（z）A（z）f = 0 are obtained, where Π（z） and A（z） are periodic entire functions with period 2πi and σe（Π） 〈 σe（A）.

A NEW DE-NOISING METHOD BASED ON 3-BAND WAVELET AND NONPARAMETRIC ADAPTIVE ESTIMATION

Wavelet de-noising has been well known as an important method of signal de-noising. Recently,most of the research efforts about wavelet de-noising focus on how to select the threshold,where Donoho method is applied widely. Compared with traditional 2-band wavelet,3-band wavelet has advantages in many aspects. According to this theory,an adaptive signal de-noising method in 3-band wavelet domain based on nonparametric adaptive estimation is proposed. The experimental results show that in 3-band wavelet domain,the proposed method represents better characteristics than Donoho method in protecting detail and improving the signal-to-noise ratio of reconstruction signal.

Predictability of Ensemble Forecasting Estimated Using the Kullback–Leibler Divergence in the Lorenz Model

A new method to quantify the predictability limit of ensemble forecasting is presented using the Kullback–Leibler(KL)divergence(also called the relative entropy), which provides a measure of the difference between the probability distributions of ensemble forecasts and local reference(true) states. The KL divergence is applicable to a non-normal distribution of ensemble forecasts, which is a substantial improvement over the previous method using the ensemble spread. An example from the three-variable Lorenz model illustrates the effectiveness of the KL divergence, which can effectively quantify the predictability limit of ensemble forecasting. On this basis, the KL divergence is used to investigate the dependence of the predictability limit of ensemble forecasting on the initial states and the magnitude of initial errors. The local predictability limit of ensemble forecasting varies considerably with the initial states, as well as with the magnitude of initial errors. Further research is needed to examine the real-world applications of the KL divergence in measuring the predictability of ensemble weather forecasts.

INVERSE EIGENVALUE PROBLEM OF HERMITIAN GENERALIZED ANTI-HAMILTONIAN MATRICES

In this paper, the inverse eigenvalue problem of Hermitian generalized anti-Hamiltonian matrices and relevant optimal approximate problem are considered. The necessary and sufficient conditions of the solvability for inverse eigenvalue problem and an expression of the general solution of the problem are derived. The solution of the relevant optimal approximate problem is given. MR Subject Classification 15A57 Keywords Hermitian generalized anti-Hamiltonian matrix - inverse eigenvalue problem - optimal approxmation Supported by the National Natural Science Foundation of China (1017103).

An Expected Patch Log Likelihood Denoising Method Based on Internal and External Image Similarity

Nonlocal property is an important feature of natural images,which means that the patch matrix formed by similar image patches is low-rank.Meanwhile,learning good image priors is of great importance for image denoising.In this paper,we combine the image self-similarity with EPLL(Expected patch log likelihood)method,and propose an EPLL denoising model based on internal and external image similarity to improve the preservation of image details.The experiment results show that the validity of our method is proved from two aspects of visual and numerical results.

Structure the radiation dose curve of continuous slope

The traditional interpolation and fitting can not restore the radiation dose curve with the radiation source characteristic. Therefore, during the reduction the field of one dimensional radiation dose, the mathematical structure method was used to restore the curve with the dose value of the radioactive source is inversely proportional to the square of the distance from the radioactive source.

Research on the Copyright Protection Technology of Digital Clothing Effect Diagram

As a kind of digital clothing design works,digital clothing effect diagram shows fast and roughly designer intent expression and needs protection of digital copyright protection technology.A novel digital clothing effect diagram copyright protection scheme and some algorithms based on digital image watermark technology are proposed,used by two-dimensional chaotic encryption watermark generation algorithm to generate watermark data,watermark embedding algorithm twice using genetic algorithm to obtain the optimal embedding position and embedding strength,and watermark verification algorithm to determine the copyright ownership.Experimental results and analyses show that the proposed scheme and algorithms have the feasibility and effectiveness.

Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

Due to the rapid increase in urbanization and population,crowd gatherings are frequently observed in the form of concerts,political,and religious meetings.HAJJ is one of the well-known crowding events that takes place every year in Makkah,Saudi Arabia.Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence(AI)applications.The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification(SSODTL-CD2C)model.The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities.To attain this,SSODTL-CD2C technique exploits Oppositional Salp Swarm Optimization Algorithm(OSSA)with EfficientNet model to derive the feature vectors.At the same time,Stacked Sparse Auto Encoder(SSAE)model is utilized for the classification of crowd densities.Finally,SSO algorithm is employed for optimal fine-tuning of the parameters involved in SSAE mechanism.The performance of the proposed SSODTL-CD2C technique was validated using a dataset with four different kinds of crowd densities.The obtained results demonstrated that the proposed SSODTLCD2C methodology accomplished an excellent crowd classification performance with a maximum accuracy of 93.25%.So,the proposed method will be highly helpful in managing HAJJ and other crowded events.