QUATERNIONIC SLICE REGULAR FUNCTIONS AND QUATERNIONIC LAPLACE TRANSFORMS

The functions studied in the paper are the quaternion-valued functions of a quaternionic variable.It is shown that the left slice regular functions and right slice regular functions are related by a particular involution,and that the intrinsic slice regular functions play a central role in the theory of slice regular functions.The relation between left slice regular functions,right slice regular functions and intrinsic slice regular functions is revealed.As an application,the classical Laplace transform is generalized naturally to quaternions in two different ways,which transform a quaternion-valued function of a real variable to a left or right slice regular function.The usual properties of the classical Laplace transforms are generalized to quaternionic Laplace transforms.

Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment

The structure and function of brain networks have been altered in patients with end-stage renal disease(ESRD).Manifold regularization(MR)only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions.To solve this issue,we developed a method to construct a dynamic brain functional network(DBFN)based on dynamic hypergraph MR(DHMR)and applied it to the classification of ESRD associated with mild cognitive impairment(ESRDaMCI).The construction of DBFN with Pearson’s correlation(PC)was transformed into an optimization model.Node convolution and hyperedge convolution superposition were adopted to dynamically modify the hypergraph structure,and then got the dynamic hypergraph to form the manifold regular terms of the dynamic hypergraph.The DHMR and L_(1) norm regularization were introduced into the PC-based optimization model to obtain the final DHMR-based DBFN(DDBFN).Experiment results demonstrated the validity of the DDBFN method by comparing the classification results with several related brain functional network construction methods.Our work not only improves better classification performance but also reveals the discriminative regions of ESRDaMCI,providing a reference for clinical research and auxiliary diagnosis of concomitant cognitive impairments.

PARTIAL REGULARITY FOR MINIMIZERS OF HIGHER ORDER QUASICONVEX FUNCTIONALS

We consider the following quasiconvex functional I(u)=∫ Gf(x,δu,D mu) d x where u is a vector valued function in W m,p (G),m>1 and p>2. The partial C m,a —regularity is proved for minimizers of I(u) under weaker conditions.

The Similarity Transformation of Regularizing Functionals and Simularity Property of Their Fourier Transformations

Let and denote respectively the functionswhere λ≥1, The author discusses the similarity transformation of the regularizing functionals of these functions and the similar property of their Fourier transformation.

APPROXIMATION ANALYSES FOR FUZZY VALUED FUNCTIONS IN L_1(μ)-NORM BY REGULAR FUZZY NEURAL NETWORKS

By defining fuzzy valued simple functions and giving L1(μ) approximations of fuzzy valued integrably bounded functions by such simple functions, the paper analyses by L1(μ)-norm the approximation capability of four-layer feedforward regular fuzzy neural networks to the fuzzy valued integrably bounded function F : Rn → FcO(R). That is, if the transfer functionσ: R→R is non-polynomial and integrable function on each finite interval, F may be innorm approximated by fuzzy valued functions defined as to anydegree of accuracy. Finally some real examples demonstrate the conclusions.

Kernel matrix learning with a general regularized risk functional criterion

Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.

Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization

Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.

Correlation between Residual Renal Function and Hypertension in Regular Haemodialysis Patients

Objective: To evaluate the correlation between residual renal function and hypertension in regular haemodialysis patients. Background: Initiating chronic dialysis treatment gives end-stage renal disease patients a new lease on life. However, the annual mortality rate in dialysis patients is ~20% and quality of life is substantially reduced. Patients and Methods: This study was carried out on a reasonable number of subjects on regular haemodialysis divided into two groups. All were given informed consent and, the study was approved by the ethics committee of Menoufia University. Results: There was significant relation between presence of residual renal function and hypertension in patients with ESRD on regular haemodialysis, but the relation between residual renal function and control of hypertension is not statistically significant. 40% of group 1 were hypertensive, 66.7% of group 2 patients were hypertensive, the interdialytic weight gain mean was 1.42 in group 1 and 2.37 in group 2. Control of hypertension was achieved in 63.6% of group 1 patients by one drug, 27.3% patients by 2 drugs;however 9.1% of patients need 3 drugs to control their blood pressure, while in group 2 40% of patients were controlled by one drug, 45% with 2 drugs and 15% need 3 drugs to control blood pressure. Conclusion: There is significant relation between presence of residual renal function and hypertension in patients with ESRD on regular haemodialysis, but the relation between residual renal function and control of hypertension is not statistically significant.

Estimation of Derivatives for Bounded Regular Vanishing Functions

In this paper, we mainly discuss the problem of estimating the n-th derivative for bounded regular vanishing functions. The estimation of the n-th derivative for the function is deduced by the 1-th and 2-th derivative.

Zeta Functions of the Complement and xyz-Transformations of a Regular Graph

Let Z(λ,G)denote the zeta function of a graph G.In this paper the complement G^Cand the G^(xyz)-transformation G^(xyz)of an r-regular graph G with n vertices and m edges for x,y,z∈{0,1,+,-},are considerd.The relationship between Z(λ,G)and Z(λ,G^C)is obtained.For all x,y,z∈{0,1,+,-},the explicit formulas for the reciprocal of Z(λ,G^(xyz))in terms of r,m,n and the characteristic polynomial of G are obtained.Due to limited space,only the expressions for G^(xyz)with z=0,and xyz∈{0++,+++,1+-}are presented here.

THE PRODUCT OPERATOR BETWEEN BLOCH-TYPE SPACES OF SLICE REGULAR FUNCTIONS

There is little work concerning the properties of quaternionic operators acting on slice regular function spaces defined on quaternions.In this paper,we present an equivalent characterization for the boundedness of the product operator C_(φ)D^(m) acting on Bloch-type spaces of slice regular functions.After that,an equivalent estimation for its essential norm is established,which can imply several existing results on holomorphic spaces.

Robust elastic impedance inversion using L1-norm misfit function and constraint regularization

The classical elastic impedance （EI） inversion method, however, is based on the L2-norm misfit function and considerably sensitive to outliers, assuming the noise of the seismic data to be the Guassian-distribution. So we have developed a more robust elastic impedance inversion based on the Ll-norm misfit function, and the noise is assumed to be non-Gaussian. Meanwhile, some regularization methods including the sparse constraint regularization and elastic impedance point constraint regularization are incorporated to improve the ill-posed characteristics of the seismic inversion problem. Firstly, we create the Ll-norm misfit objective function of pre-stack inversion problem based on the Bayesian scheme within the sparse constraint regularization and elastic impedance point constraint regularization. And then, we obtain more robust elastic impedances of different angles which are less sensitive to outliers in seismic data by using the IRLS strategy. Finally, we extract the P-wave and S-wave velocity and density by using the more stable parameter extraction method. Tests on synthetic data show that the P-wave and S-wave velocity and density parameters are still estimated reasonable with moderate noise. A test on the real data set shows that compared to the results of the classical elastic impedance inversion method, the estimated results using the proposed method can get better lateral continuity and more distinct show of the gas, verifying the feasibility and stability of the method.

The Ill-Posedness of Derivative Interpolation and Regularized Derivative Interpolation for Non-Bandlimited Functions

In this paper, the ill-posedness of derivative interpolation is discussed, and a regularized derivative interpolation for non-bandlimited signals is presented. The convergence of the regularized derivative interpolation is studied. The numerical results are given and compared with derivative interpolation using the Tikhonov regularization method. The regularized derivative interpolation in this paper is more accurate in computation.

α-times Integrated Regularized Cosine Functions and Second Order Abstract Cauchy Problems

In this paper, α-times integrated C-regularized cosine functions and mild α-times integrated C-existence families of second order are introduced. Equivalences are proved among α-times integrated C-regularized cosine function for a linear operator A, C-wellposed of (α+1)-times abstract Cauchy problem and mild a -times integrated C-existence family of second order for A when the commutable condition is satisfied. In addition, if A = C-1AC, they are also equivalent to A generating the α -times integrated C-regularized cosine function. The characterization of an exponentially bounded mild α -times integrated C-existence family of second order is given out in terms of a Laplace transform.

Regularity of Continuous Linear Operators on Banach Function Spaces

In this paper we present some characterizations of Banach function spaces on which every continuous linear operator is regular.

CIDP: a multi-functional platform for designing CRISPR sgRNAs

Dear Editor,Compared with traditional technologies affecting gene expression,changing DNA sequences of target genes is one of the most outstanding characters of CRISPR(Clustered Regularly Interspaced Short Palindromic Repeats).Single-guide RNAs(sgRNAs)guiding endonuclease Cas to target sites is a crucial step of CRISPR-Cas system for changing DNA sequences.An ideal sgRNA should only bind to the target gene.However,similar sequences of non-target sites can also be recognized leading to off-target effects[1].

Regularization Methods to Approximate Solutions of Variational Inequalities

In this paper, we study the regularization methods to approximate the solutions of the variational inequalities with monotone hemi-continuous operator having perturbed operators arbitrary. Detail, we shall study regularization methods to approximate solutions of following variational inequalities: and with operator A being monotone hemi-continuous form real Banach reflexive X into its dual space X*, but instead of knowing the exact data (y0, A), we only know its approximate data satisfying certain specified conditions and D is a nonempty convex closed subset of X;the real function f defined on X is assumed to be lower semi-continuous, convex and is not identical to infinity. At the same time, we will evaluate the convergence rate of the approximate solution. The regularization methods here are different from the previous ones.

基于GRACE星间重力位差的Slepian局部地表质量变化反演法

根据Slepian基函数在频域和空域的局部集中特性,建立了利用GRACE星间重力位差(GPD)估计局部地表质量变化的Slepian基函数反演模型及病态问题求解算法,并以亚马逊流域的陆地水储量变化(TWSC)反演为例,评估了反演方法的精度和有效性.首先,通过闭环数值模拟比较了Slepian基函数法(GPD SBF)、基于GPD的mascon方法(GPD Mascon)和球谐系数法(GPD SH)反演2005年亚马逊流域TWSC的性能,结果表明:Slepian基函数法通过限定信号的频域和空域范围,降低了反演过程中向下延拓带来的不确定性,有效削弱了法方程求解的病态性,其解算结果的稳定性明显优于GPD Mascon;GPD SBF的反演精度及可靠性总体上优于GPD Mascon和GPD SH,并且能够更好地恢复边缘区域的信号和减小泄漏误差的影响.其次,利用实测的GRACE GPD数据反演了2004—2015年亚马逊流域的TWSC时间序列,结果显示:GPDSBF相比GPDMascon反演的TWSC与官方mascon模型(CSR、JPL和GSFC RL06 mascon)更为一致,并且GPD SBF反演结果呈现出更多的空间细节和更好的信噪比.最后,利用不同GRACE反演结果计算的(TWSC的一阶导数)和水文气象数据(GPCP降水、ERA5蒸散发)由水量平衡方程估计了亚马逊流域Obidos水文站的月平均径流量,并采用测站的实测径流量进行检验,结果表明:GPD SBF和GPD Mascon估计的径流量与实测值扣除季节性信号后的差值STD分别为12.35 mm和14.54 mm,相关系数分别为0.71和0.69,并且GPD SBF和各种官方mascon模型估计的径流量与实测径流量更为接近.本文研究证明Slepian基函数法可削弱病态问题求解对正则化约束的依赖,其比传统的GPD Mascon解算精度和可靠性更高,为反演高精度、高分辨率的局部地表质量变化提供了一种新的解决方案.

腹腔镜规则性肝叶切除术对原发性肝癌患者肝功能、免疫功能、血清AFP、Hcy水平的影响

目的:探究腹腔镜规则性肝叶切除术对原发性肝癌(PLC)患者肝功能、免疫功能、血清甲胎蛋白(AFP)、同型半胱氨酸(Hcy)水平的影响。方法:回顾性分析2019年1月—2023年6月于贵州省人民医院接受手术治疗的200例PLC患者的临床资料,将行开腹手术的100例患者纳入对照组,行腹腔镜规则性肝叶切除术的100例患者纳入观察组。观察两组术后肝功能[总胆红素(TBIL)、白蛋白(ALB)、丙氨酸转移酶(ALT)、天冬氨酸氨基转移酶(AST)]、免疫功能(CD8^(+)、CD4^(+)、CD3^(+))、炎症因子[肿瘤坏死因子-α(TNF-α)、白细胞介素-6(IL-6)]及血清AFP、Hcy水平的变化情况。结果:术前,两组肝功能、免疫功能、炎症因子、血清AFP、Hcy水平比较,差异无统计学意义(P>0.05);术后1 d,两组ALB高于术前,TBIL、ALT、AST及术后5 d的AFP、Hcy均低于术前,且研究组ALB高于对照组,TBIL、ALT、AST、AFP、Hcy低于对照组,差异有统计学意义(P<0.05);术后5 d,对照组TNF-α、IL-6水平均高于术前,CD8^(+)、CD4^(+)、^(+)CD3水平均低于术前,差异有统计学意义(P<0.05),术后5 d,研究组TNF-α、IL-6水平均低于对照组,CD8^(+)、CD4^(+)、^(+)CD3水平均高于对照组,差异有统计学意义(P<0.05)。结论:PLC患者予以腹腔镜规则性肝叶切除术治疗可明显降低患者术后血清AFP、Hcy水平,降低炎症因子水平,提高免疫力,恢复肝功能。

基于正则模糊划分的模糊系统及其逼近性质

文中讨论了带有不同模糊基函数的模糊系统的逼近问题。首先,基于一维正则模糊划分和重叠函数建立多维正则模糊划分,以划分中的元素为模糊基函数设计模糊系统,应用Weierstrass逼近定理证明了该模糊系统是通用逼近器,给出了模糊系统的逼近误差界。其次,提出了多项式型、指数型和对数型模糊系统,并给出了带有隶属函数参数的逼近误差界。最后,通过数值实验对不同模糊系统的逼近能力进行了比较,实验结果进一步验证了理论分析的正确性。