Receded near point of convergence as a predictor of mild cognitive impairment in the general geriatric population:results from a population-based study

AIM:To investigate the relationship between near point of convergence(NPC)and mild cognitive impairment(MCI)in the general elderly population.METHODS:The present report is a part of the Tehran Geriatric Eye Study(TGES):a population-based crosssectional study conducted on individuals 60 years of age and above living in Tehran,Iran using the multi-stage stratified random cluster sampling method.Cognitive status was assessed using the Persian version of the Mini-Mental State Examination(MMSE).All study participants underwent complete ocular examination including measurement of uncorrected and best-corrected visual acuity,objective and subjective refraction,cover testing,NPC measurement,and slit-lamp biomicroscopy.RESULTS:The data of 1190 individuals were analyzed for this report.The mean age of the participants analyzed was 66.82±5.42(60-92y)and 728(61.2%)of them were female.Patients with MCI had a significantly more receded NPC compared to subjects with normal cognitive status(10.89±3.58 vs 7.76±2.71 cm,P<0.001).In the multivariable logistic regression model and in the presence of confounding variables,a receded NPC was statistically significantly associated with an increased risk of MCI(odds ratio:1.334,95%confidence interval:1.263 to 1.410,P<0.001).According to receiver operating characteristic(ROC)analysis,a cut point NPC>8.5 cm(area under the curve:0.764,P<0.001)could predict the presence of MCI with a sensitivity and specificity of 70.9%and 69.5%,respectively.CONCLUSION:A receded NPC can be clinically proposed as a predictor of MCI in older adults.It is recommended that elderly with a receded NPC>8.50 cm undergo detailed cognitive screening for a definite diagnosis of MCI.In this case,the necessary interventions can be carried out to slow down MCI progression to dementia.

RAF near point rule for near point of convergence-a short review

RAF near point rule (RNPR) is a routinely used instrument in ophthalmology and optometry practice as well as for research purposes to measure the near point of convergence (NPC). The measurement of NPC is an important criterion for diagnosis and management of convergence insufficiency. The RNPR forms an important tool for ophthalmic clinicians however, only a very little is understood about it. This article tries to describe and review the designs, measurement techniques, merits and demerits of the RNPR and establish the need for its modification. It recommends that clinicians and researchers consider these findings while measuring NPC with the RNPR.

The Convergence Characteristic of the Forward I-V Characteristic Curves of a Semiconductor Silicon Barrier at Different Temperatures

The /-V-（T） characteristic curves of p-n junctions with the forward voltage as the independent variable, the logarithm of forward current as the dependent variable, and the junction temperature as the parameter, almost converge at one point in the first quadrant. The voltage corresponding with the convergence point nearly equals the bandgap of the semiconductor material. This convergence point can be used to obtain the I-V characteristic curve at any temperature.

Proximal point algorithm for a new class of fuzzy set-valued variational inclusions with (H,η)-monotone mappings

We introduced a new class of fuzzy set-valued variational inclusions with (H,η)-monotone mappings. Using the resolvent operator method in Hilbert spaces, we suggested a new proximal point algorithm for finding approximate solutions, which strongly converge to the exact solution of a fuzzy set-valued variational inclusion with (H,η)-monotone. The results improved and generalized the general quasi-variational inclusions with fuzzy set-valued mappings proposed by Jin and Tian Jin MM, Perturbed proximal point algorithm for general quasi-variational inclusions with fuzzy set-valued mappings, OR Transactions, 2005, 9(3): 31-38, (In Chinese); Tian YX, Generalized nonlinear implicit quasi-variational inclusions with fuzzy mappings, Computers & Mathematics with Applications, 2001, 42: 101-108.

CONVERGENCE OF THE POINT VORTEX METHODS FOR EULER EQUATION ON HALF PLANE

In this paper, we study the point vortex method for 2-D Euler equation of incompressible how on the half plane, and the explicit Euler's scheme is considered with the reflection method handling the boundary condition. Optimal error bounds for this fully discrete scheme are obtained.

Efficient Numerical Scheme for Solving Large System of Nonlinear Equations

A fifth-order family of an iterative method for solving systems of nonlinear equations and highly nonlinear boundary value problems has been developed in this paper.Convergence analysis demonstrates that the local order of convergence of the numerical method is five.The computer algebra system CAS-Maple,Mathematica,or MATLAB was the primary tool for dealing with difficult problems since it allows for the handling and manipulation of complex mathematical equations and other mathematical objects.Several numerical examples are provided to demonstrate the properties of the proposed rapidly convergent algorithms.A dynamic evaluation of the presented methods is also presented utilizing basins of attraction to analyze their convergence behavior.Aside from visualizing iterative processes,this methodology provides useful information on iterations,such as the number of diverging-converging points and the average number of iterations as a function of initial points.Solving numerous highly nonlinear boundary value problems and large nonlinear systems of equations of higher dimensions demonstrate the performance,efficiency,precision,and applicability of a newly presented technique.

Attractors and the attraction basins of discrete-time cellular neural networks

The dynamic behavior of discrete-time cellular neural networks(DTCNN), which is strict with zero threshold value, is mainly studied in asynchronous mode and in synchronous mode. In general, a k-attractor of DTCNN is not a convergent point. But in this paper, it is proved that a k-attractor is a convergent point if the strict DTCNN satisfies some conditions. The attraction basin of the strict DTCNN is studied, one example is given to illustrate the previous conclusions to be wrong, and several results are presented. The obtained results on k-attractor and attraction basin not only correct the previous results, but also provide a theoretical foundation of performance analysis and new applications of the DTCNN.

New iterative schemes for strongly relatively nonexpansive mappings and maximal monotone operators

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

On nonparametric change point estimator based on empirical characteristic functions

We propose a nonparametric change point estimator in the distributions of a sequence of independent observations in terms of the test statistics given by Huˇskov′a and Meintanis(2006) that are based on weighted empirical characteristic functions. The weight function ω(t; a) under consideration includes the two weight functions from Huˇskov′a and Meintanis(2006) plus the weight function used by Matteson and James(2014),where a is a tuning parameter. Under the local alternative hypothesis, we establish the consistency, convergence rate, and asymptotic distribution of this change point estimator which is the maxima of a two-side Brownian motion with a drift. Since the performance of the change point estimator depends on a in use, we thus propose an algorithm for choosing an appropriate value of a, denoted by a_s which is also justified. Our simulation study shows that the change point estimate obtained by using a_s has a satisfactory performance. We also apply our method to a real dataset.

Approximate Augmented Lagrangian Functions and Nonlinear Semidefinite Programs

In this paper, an approximate augmented Lagrangian function for nonlinear semidefinite programs is introduced. Some basic properties of the approximate augmented Lagrange function such as monotonicity and convexity are discussed. Necessary and sufficient conditions for approximate strong duality results are derived. Conditions for an approximate exact penalty representation in the framework of augmented Lagrangian are given. Under certain conditions, it is shown that any limit point of a sequence of stationary points of approximate augmented Lagrangian problems is a KKT point of the original semidefinite program and that a sequence of optimal solutions to augmented Lagrangian problems converges to a solution of the original semidefinite program.