Ultrastructural and Immunohistochemical Characteristics of Corneal Lenticule Extracted during Correction of Residual Myopia in the Long-Term Period after SMILE

Purpose: To evaluate ultrastructural characteristics of lenticule surface extracted during correction of residual myopia in patients after small-incision lenticule extraction (SMILE). Methods and material: This study had a prospective, consecutive, comparative design. Sixteen patients (16 eyes) underwent additional intervention for residual myopia correction after SMILE. 16 specimens of removed lenticules underwent morphological examination. Markers and reagents were used to determine actin microfilaments, neutral fats and cell nuclei. The tissue was analyzed in layers in 2D slices form, volumetric Z-stacks, or selected areas were formed in orthogonal projections. The surface of the extracted lenticule was analyzed using scanning electron microscopy. Patients’ refractive outcomes were measured postoperatively (1 day;1 and 3 months). Results: Postoperatively uncorrected distance visual acuity (20/20 or better) was in 100% cases 3 months after surgery. Ultrastructural studies have shown the difference in surfaces of the newly formed lenticule. Structural changes of the posterior lenticule surface were characterized by ruptures of collagen fibers on its surface, degenerative changes in keratocytes with signs of colliquation necrosis, cell apoptosis and F-actin in cell cytoplasm. Conclusion: Collagen fibers are immersed in the stroma on the anterior surface of the lenticule. There is no complete structure restoration of collagen fibers explaining the lack of tight adhesion of anterior and posterior surfaces of the intrastromal space even in the long-term postoperative period. There are no degenerative changes of keratocytes on the anterior lenticule surface, that is, their changes in SMILE are reversible in most cases.

A time domain induced polarization relaxation time spectrum inversion method based on a damping factor and residual correction

Relaxation time spectra （RTS） derived from time domain induced polarization data （TDIP） are helpful to assess oil reservoir pore structures. However, due to the sensitivity to the signal-to-noise ratio （SNR）, the inversion accuracy of the traditional singular value decomposition （SVD） inversion method reduces with a decrease of SNR. In order to enhance the inversion accuracy and improve robustness of the inversion method to the SNR, an improved inversion method, based on damping factor and spectrum component residual correction, is proposed in this study. The numerical inversion results show that the oscillation of the RTS derived from the SVD method increased with a decrease of SNR, which makes it impossible to get accurate inversion components. However, the SNR has little influence on inversion components of the improved method, and the RTS has high inversion accuracy and robustness. Moreover, RTS derived from core sample data is basically in accord with the pore-size distribution curve, and the RTS derived from the actual induced polarization logging data is smooth and continuous, which indicates that the improved method is practicable.

Residual Correction Procedure with Bernstein Polynomials for Solving Important Systems of Ordinary Differential Equations

One of the most attractive subjects in applied sciences is to obtain exact or approximate solutions for different types of linear and nonlinear systems.Systems of ordinary differential equations like systems of second-order boundary value problems(BVPs),Brusselator system and stiff system are significant in science and engineering.One of the most challenge problems in applied science is to construct methods to approximate solutions of such systems of differential equations which pose great challenges for numerical simulations.Bernstein polynomials method with residual correction procedure is used to treat those challenges.The aim of this paper is to present a technique to approximate solutions of such differential equations in optimal way.In it,we introduce a method called residual correction procedure,to correct some previous approximate solutions for such systems.We study the error analysis of our given method.We first introduce a new result to approximate the absolute solution by using the residual correction procedure.Second,we introduce a new result to get appropriate bound for the absolute error.The collocation method is used and the collocation points can be found by applying Chebyshev roots.Both techniques are explained briefly with illustrative examples to demonstrate the applicability,efficiency and accuracy of the techniques.By using a small number of Bernstein polynomials and correction procedure we achieve some significant results.We present some examples to show the efficiency of our method by comparing the solution of such problems obtained by our method with the solution obtained by Runge-Kutta method,continuous genetic algorithm,rational homotopy perturbation method and adomian decomposition method.

A Data-Driven Rutting Depth Short-Time Prediction Model With Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.

Aerodynamic Design for Three-Dimensional Multi-lifting Surfaces at Transonic Flow

An aerodynamic design method and corresponding codes are developed for three-dimensional multi lifting surfaces at transonic flow. It is based on the ＂iterative residual correction＂ concept that is successfully used for transonic wing design and subsonic multi-lifting surface design. The up-wind scheme is introduced into governing equations of multi-lifting surface design method and automatically acted when supersonic flow appears on the surface. A series of interface codes are programmed, including a target-pressure modification tool. Using the improved inverse aerodynamic design code, TAU code and interface codes, the transonic multi-lifting aerodynamic design software system is founded. Two cases of canard-wing configuration have been performed to validate the method and codes. The results show that the convergence of analysis/design iteration is very good at higher speed transonic flow.

A recognition model of survival situations for survivable systems

Due to the lack of pre-recognition and post- prediction in existing survivable systems, a recognition model of survival situations for survivable systems is proposed. First, the survival situation data is clustered into several survival clusters with different service levels based on the Ward method, and then the survival clusters are classified and recognized by means of the error-eliminating decision-making method, which can realize the pre-recognition of the system＇s survival situation. Secondly, the differentiated survival situation data is used to generate stationary predicting sequences. The autoregressive integrated moving average （ARIMA） model is constructed, and the stability, randomness and reversibility index of the model are verified by the auto- correlation function and partial auto-correlation function. Finally, fuzzy particles and the residual correction for the support vector regression （SVR） model are applied to realize the post-prediction of the survival situation. Compared with traditional decision-making methods, the simulation experiments show that the pre-recognition module can not only cluster the survival situation data and identify the service ranks, but can also recognize the illegal users. According to the prediction of abnormal situations numbers and residual correction, the model can effectively realize the post- prediction of survival situations for survivable systems.

A Port Ship Flow Prediction Model Based on the Automatic Identification System and Gated Recurrent Units

Water transportation today has become increasingly busy because of economic globalization.In order to solve the problem of inaccurate port traffic flow prediction,this paper proposes an algorithm based on gated recurrent units(GRUs)and Markov residual correction to pass a fixed cross-section.To analyze the traffic flow of ships,the statistical method of ship traffic flow based on the automatic identification system(AIS)is introduced.And a model is put forward for predicting the ship flow.According to the basic principle of cyclic neural networks,the law of ship traffic flow in the channel is explored in the time series.Experiments have been performed using a large number of AIS data in the waters near Xiazhimen in Zhoushan,Ningbo,and the results show that the accuracy of the GRU-Markov algorithm is higher than that of other algorithms,proving the practicability and effectiveness of this method in ship flow prediction.

Annual precipitation gray forecast in disaster year of Chedaren debris flow

The Chedaren ravine belongs to high-prone areas of debris flow in Jilin Province, which threaten the local people＇ s life and security seriously. The authors used the residual correction theory to amend the GM （ 1, 1 ） model and forecast annual precipitation in disaster year of the Chedaren ravine ; it provides scientific foundation for early warning of debris flow disaster in the rainy season based on weather forecast. The prediction resuits show that annual precipitation is 724.7 mm in 2009 ; the region will probably occur large-scale debris flow during the rainy season.

Static Corrections Methods in the Processing of Deep Reflection Seismic Data

Statics are big challenges for the processing of deep reflection seismic data. In this paper several different statics solutions have been implemented in the processing of deep reflection seismic data in South China and their corresponding results have been compared in order to find proper statics solutions. Either statics solutions based on tomographic principle or combining the low-frequency components of field statics with the high-frequency ones of refraction statics can provide reasonable statics solutions for deep reflection seismic data in South China with very rugged surface topography, and the two statics solutions can correct the statics anomalies of both long spatial wavelengths and short ones. The surface-consistent residual static corrections can serve as the good compensations to the several kinds of the first statics solutions. Proper statics solutions can improve both qualities and reso- lutions of seismic sections, especially for the reflections of Moho in the upmost mantle.

Accuracy characterization of Shack-Hartmann sensor with residual error removal in spherical wavefront calibration

The widely used Shack-Hartmann wavefront sensor(SHWFS)is a wavefront measurement system.Its measurement accuracy is limited by the reference wavefront used for calibration and also by various residual errors of the sensor itself.In this study,based on the principle of spherical wavefront calibration,a pinhole with a diameter of 1μm was used to generate spherical wavefronts with extremely small wavefront errors,with residual aberrations of 1.0×10^(−4)λRMS,providing a high-accuracy reference wavefront.In the first step of SHWFS calibration,we demonstrated a modified method to solve for three important parameters(f,the focal length of the microlens array(MLA),p,the sub-aperture size of the MLA,and s,the pixel size of the photodetector)to scale the measured SHWFS results.With only three iterations in the calculation,these parameters can be determined as exact values,with convergence to an acceptable accuracy.For a simple SHWFS with an MLA of 128×128 sub-apertures in a square configuration and a focal length of 2.8 mm,a measurement accuracy of 5.0×10^(−3)λRMS was achieved across the full pupil diameter of 13.8 mm with the proposed spherical wavefront calibration.The accuracy was dependent on the residual errors induced in manufacturing and assembly of the SHWFS.After removing these residual errors in the measured wavefront results,the accuracy of the SHWFS increased to 1.0×10^(−3)λRMS,with measured wavefronts in the range ofλ/4.Mid-term stability of wavefront measurements was confirmed,with residual deviations of 8.04×10^(−5)λPV and 7.94×10^(−5)λRMS.This study demonstrates that the modified calibration method for a high-accuracy spherical wavefront generated from a micrometer-scale pinhole can effectively improve the accuracy of an SHWFS.Further accuracy improvement was verified with correction of residual errors,making the method suitable for challenging wavefront measurements such as in lithography lenses,astronomical telescope systems,and adaptive optics.

Forecasting oil production in unconventional reservoirs using long short term memory network coupled support vector regression method: A case study

Production prediction is crucial for the recovery of hydrocarbon resources.However,accurate and rapid production forecasting remains challenging for unconventional reservoirs due to the complexity of the percolation process and the scarcity of available data.To address this problem,a novel model combining a long short-term memory network(LSTM)and support vector regression(SVR)was proposed to forecast tight oil production.Three variables,the tubing head pressure,nozzle size,and water rate were utilized as the inputs of the presented machine-learning workflow to account for the influence of operational parameters.The time-series response of tight oil production was the output and was predicted by the optimized LSTM model.An SVR-based residual correction model was constructed and embedded with LSTM to increase the prediction accuracy.Case studies were carried out to verify the feasibility of the proposed method using data from two wells in the Ma-18 block of the Xinjiang oilfield.Decline curve analysis(DCA)methods,LSTM and artificial neural network(ANN)models were also applied in this study and compared with the LSTM-SVR model to prove its superiority.It was demonstrated that introducing residual correction with the newly proposed LSTM-SVR model can effectively improve prediction performance.The LSTM-SVR model of Well A produced the lowest prediction root mean square error(RMSE)of 5.42,while the RMSE of Arps,PLE Duong,ANN,and LSTM were 5.84,6.65,5.85,8.16,and 7.70,respectively.The RMSE of Well B of LSTM-SVR model is 0.94,while the RMSE of ANN,and LSTM were 1.48,and 2.32.

XCO_2 satellite retrieval experiments in short-wave and infrared spectra with SCIATRAN model for Sahara Desert

The spectra of O_2 A-band(0.76 μm) and CO_2 near-infrared emissions(1.6 μm) are simulated by the SCIATRAN radiative transfer model(V3.1.23), and compared with those observed by GOSAT-FTS(Greenhouse gases Observing SATellite-Fourier Transform Spectrometer). Systematic deviations between the observed and simulated spectra are found to exist,especially for the O_2 A-band. The discrepancies are characterized by their mean differences averaged over the observed spectral ranges. A correction is applied to the observed GOSAT-FTS L1B(V141.141) spectra by scaling the spectral intensity measured by TANSO-FTS(Thermal and Near infrared Sensor for carbon Observation Fourier Transform Spectrometer) onboard GOSAT.The average columnar CO_2 concentrations(XCO_2) are retrieved from the observed and the corrected GOSAT-FTS spectra by using the SCIATRAN inversion algorithm. Compared with the GOSAT-FTS L2 XCO_2 data products retrieved from the observed spectra of GOSAT-FTS, the SCIATRAN retrievals from the corrected spectra show a much better agreement, with the relative error less than 1%. But the results of GOSAT TANSO-FTS(V161.160) show smaller residuals than GOSAT TANSO-FTS(V141.141) without mean residual correction. The results indicate that the mean residual correction would increase the precision of XCO_2 retrieval for spectra with systematic deviations.

A numerical method for solutions of Lotka-Volterra predator-prey model with time-delay

In this paper, we present a numerical scheme to obtain polynomial approximations for the solutions of continuous time-delayed population models for two interacting species. The method includes taking inner product of a set of monomials with a vector obtained from the problem under consideration. Doing this, the problem is transformed to a non- linear system of algebraic equations. This system is then solved, yielding coefficients of the approximate polynomial solutions. In addition, the technique of residual correction, which aims to increase the accuracy of the approximate solution by estimating its error, is discussed in some detail. The method and the residual correction technique are illustrated with two examples.