Single-step transepithelial photorefractive keratectomy in high myopia：qualitative and quantitative visual functions

AIM：To investigate quantitative and qualitative optical outcomes of single-step transepithelial photorefractive keratectomy（Trans PRK）in high myopia.METHODS：In a prospective interventional case-series,30 eyes with high myopia（-6.00 to-8.75 D）with（up to-3.00 D）or without astigmatism were enrolled from Bina Eye Hospital,Tehran,Iran. One-step Trans PRK was performed with aberration-free aspherical optimized profile and SCHWIND AMARIS 500 laser. One-year follow-up results for refraction,visual acuities,vector analysis,ocular wavefront（OWF）and corneal wave-front（CWF）higher order aberrations（HOA）,contrast sensitivity（CS）,and postoperative haze were assessed.RESULTS：After the surgery,both photopic and mesopic CSs significantly improved（both P〈0.001）. We detected significant induction of OWF coma and trefoil（P〈0.001 for both）HOAs; CWF coma（P=0.002）,spherical（P〈0.001）,and tetrafoil（P=0.003）HOAs in 6 mm analysis diameter; and CWF trefoil（P=0.04）HOA in 4 mm analysis diameter. The range of mean induction observed for various HOAs was0.005-0.11 μm. The 86.7% of eyes reached an uncorrected distance visual acuity of 20/20 or better; 96.7% of eyes were within ±0.5 D of targeted spherical refraction. In vector analysis,mean correction index value was 1.03 and mean index of success was 0.22. By 12mo after the operation,no eye lost any number of corrected distancevisual acuity lines. We detected no corneal haze greater than 1~＋ throughout the follow-up.CONCLUSION：Our findings show promising effects of single-step Trans PRK on quality of vision in high myopic eyes. It also improves refraction and visual acuity.

A Method on Analog Circuit Fault Diagnosis with Tolerance

In this paper, it is proved that the direction of the node-voltage difference vector, which is the difference between the node-voltage vector at faulty state and the one at the nominal state, is determined only by the location of the faulty clement in linear analog circuits. Considering that the direction of the node-voltage sensitivity vector is the same as the one of the node-voltage difference vector and also considering that the module of the node-voltage sensitivity vector presents the weight of the parameter of faulty element deviation relative to the voltage difference, fault dictionary is set up based on node-voltage sensitivity vectors. A decision algorithm is proposed concerned with both the location and the parameter difference of the faulty element. Single fault and multi-fault can be diagnosed while the circuit parameters deviate within the tolerance range of 10 %.

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen（T-N） impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks（ANNs） and support vector machines（SVMs）, in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination（R^2）, Nash-Sutcliff efficiency（NSE）, relative efficiency criteria（d rel）. Additionally, Latin-Hypercube one-factor-at-a-time（LH-OAT） and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.