Finite Element Modelling of a Reflection Differential Split-D Eddy Current Probe Scanning Surface Notches.

Differential eddy current probes are commonly used to detect shallow surface cracks in conductive materials. In recent years, a growing number of research works on their numerical modelling was conducted since the development of analytical or semi-analytical models for such a sensor may be prone to intractable complications. In this paper finite element modelling (FEM) has been employed to simulate the interaction of a reflection differential split-D probe with surface electrical discharge machined (EDM) notches in 3-dimensional (3-D) half-space. In order to attain a better insight into the correct setup of the FEM parameters, a simple multi-turn cylindrical absolute coil has also been modelled. The outcome generated through the simulated scan of this absolute coil over a surface notch in aluminum is validated with existing experimental impedance data taken from the literature. Parameters contributing to reliable FEM simulation results, such as maximum mesh size, mesh distribution, the extent of the surrounding air domain and conductivity of the air are investigated for the 3-D modelling of both absolute and differential probes. This study shows that the simulation results on a commercial reflection differential split-D surface pencil probe closely estimate the experimental measurements of the probe's impedance variations as it scans three EDM notches having different depths in aluminum. The simulation results, generated by Comsol Multiphysics FEM package (COMSOL I, COMSOL multiphysics reference manual, version 5.3, COMSOL AB, 2018, www.comsol.com), for the cases of absolute and differential probes are checked for their extent of validity.

Adaptive Neuro-fuzzy Inference System Trained for Sizing Semi-elliptical Notches Scanned by Eddy Currents.

The present study explores the capability of COMSOL Multiphysics, as a finite element modelling (FEM) tool, to model the interaction between a split-D differential surface eddy current (ECT) probe and semi-elliptical surface electrical discharge machined (EDM) notches. The effect of the small probe's lift-off and tilt on its signal is investigated through modelling and subsequently, the simulation outcomes are validated using the probe's impedance measurements. In the next stage, an adaptive neuro-fuzzy inference system (ANFIS) is designed to take the signal features as inputs and consequently, provide the length of the scanned notch as the system's output. The system is trained by extracted features of thirty model-generated signals obtained from scanning of the same number of semi-elliptical notches by means of the split-D probe. The trained ANFIS is tested afterwards using the measured signals of 3 calibration EDM notches together with 5 model-based ones. A very low average estimation error is observed with regard to the length estimation of the test notches and the accuracy of the length estimation is found to be quite reasonable.

Effect of combined ursodeoxycholic acid and glucocorticoid on the outcome of Kasai procedure: A systematic review and meta-analysis.

INTRODUCTION: Multiple studies have investigated the effect of ursodeoxycholic acid (UDCA) or glucocorticoid (GC) on the outcome of the hepatoportoenterostomy (Kasai procedure) in patients with biliary atresia (BA). However, the combined effect of these drugs (UDCA + GC) is little understood. METHODS: This meta-analysis specifically evaluated the effect of UDCA + GC after the Kasai procedure in patients with BA. A comprehensive literature search was conducted for all relevant articles in the electronic databases Medline, PubMed, Cochrane, Excerpta Medica Database (EMBASE), China National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database on Disc (CBM-disc), and Vendor Information Pages (VIP). RESULTS: Eight studies with BA patients were finally included in our meta-analysis. The 8 identified studies consisted of 3 case-control, 3 cohort, and 2 randomized controlled trials (RCTs) with overall 530 subjects (144, 152, and 234 subjects, respectively). Among them, 312 patients were treated with UDCA + GC, while 218 received placebo or other intervention. The meta-analysis indicated that groups that received UDCA + GC had significantly lower rates of postoperative jaundice relative to the controls (pooled, odds ratio [OR] = 2.41; 95% confidence interval [CI] 1.44-4.04; Z = 3.34; P = .0008), while rates of cholangitis were similar (pooled, OR = 0.87; 95% CI 0.43-1.74; Z = 0.40; P = .69). CONCLUSIONS: Combined UDCA and GC intervention was superior to that of the control in accelerating the clearance of serum bilirubin in patients with BA after the Kasai procedure. However, this conclusion requires further confirmation using RCTs of high methodological quality.

Identification and Control for Singularly Perturbed Systems Using Multitime-Scale Neural Networks.

Many well-established singular perturbation theories for singularly perturbed systems require the full knowledge of system model parameters. In order to obtain an accurate and faithful model, a new identification scheme for singularly perturbed nonlinear system using multitime-scale recurrent high-order neural networks (NNs) is proposed in this paper. Inspired by the optimal bounded ellipsoid algorithm, which is originally designed for discrete-time systems, a novel weight updating law is developed for continuous-time NNs identification process. Compared with other widely used gradient-descent updating algorithms, this new method can achieve faster convergence, due to its adaptively adjusted learning rate. Based on the identification results, a control scheme using singular perturbation theories is developed. By using singular perturbation methods, the system order is reduced, and the controller structure is simplified. The closed-loop stability is analyzed and the convergence of system states is guaranteed. The effectiveness of the identification and the control scheme is demonstrated by simulation results.

Changing Strabismus Surgery Distribution at Shanxi Province Eye Hospital in Central China.

PURPOSE: To investigate whether there has been a changing trend of strabismus surgery in a provincial eye hospital in the central part of China over the past decade. METHODS: This was a retrospective analysis of data on all strabismus surgery performed in Shanxi Province Eye Hospital in China during the past 10 years. Recorded characteristics included patient demographics, type of strabismus, age, and gender. RESULTS: A total of 12,327 patients received strabismus surgery in the eye hospital from 2005 to 2014. The number of surgeries increased steadily from 800 in 2005 to 1,723 in 2014 (P < .01). Constant exotropia was the most common type among all types of strabismus. Superior oblique muscle palsy was the most common type of paralytic strabismus. Exotropia oblique A- and V-pattern type was the most common type of special strabismus. CONCLUSIONS: The amount of strabismus surgery increased during the past decade. The proportion of patients with intermittent exotropia increased over time compared to those with other types of strabismus. [J Pediatr Ophthalmol Strabismus. 2017;54(2):112-116.].

Nonlinear systems identification and control via dynamic multitime scales neural networks.

This paper deals with the adaptive nonlinear identification and trajectory tracking via dynamic multilayer neural network (NN) with different timescales. Two NN identifiers are proposed for nonlinear systems identification via dynamic NNs with different timescales including both fast and slow phenomenon. The first NN identifier uses the output signals from the actual system for the system identification. In the second NN identifier, all the output signals from nonlinear system are replaced with the state variables of the NNs. The online identification algorithms for both NN identifier parameters are proposed using Lyapunov function and singularly perturbed techniques. With the identified NN models, two indirect adaptive NN controllers for the nonlinear systems containing slow and fast dynamic processes are developed. For both developed adaptive NN controllers, the trajectory errors are analyzed and the stability of the systems is proved. Simulation results show that the controller based on the second identifier has better performance than that of the first identifier.

Green's function method applied to the paramagnetic properties of R(1-x)X(x)MnO(3).

The magnetic properties of the isotropic manganites R(1-x)X(x)MnO(3) are studied in the paramagnetic regime using the Green's function method. The Curie-Weiss and critical temperatures, Θ and T(c), are obtained within the random phase approximation, as well as the high-temperature susceptibility. Our results are in agreement with other theoretical and experimental results.

A novel sliding-mode control of induction motor using space vector modulation technique.

This paper presents a novel sliding-mode control method for torque control of induction motors. The control principle is based on sliding-mode control combined with space vector modulation technique. The sliding-mode control contributes to the robustness of induction motor drives, and the space vector modulation improves the torque, flux, and current steady-state performance by reducing the ripple. The Lyapunov direct method is used to ensure the reaching and sustaining of sliding mode and stability of the control system. The performance of the proposed system is compared with those of conventional sliding-mode controller and classical PI controller. Finally, computer simulation results show that the proposed control scheme provides robust dynamic characteristics with low torque ripple.