Background: As photochemical reaction that can stiffen the cornea, CXL is the only promising method of preventing progression of keratectasia such as KC and secondary ectasia following refractive surgery. The aim of CXL is to stabilize the underlying condition with a small chance of visual improvement. Objective: To show the sequences of changes in visual acuity and topographic outcomes during 1 year post CXL for patients with progressive Keratoconus.Type of the study: Cross sectional studyMethods: CXL procedure was done for 45 eyes with progressive KC. The following parameters had been monitored pre operatively, 1, 3, 6 and 12 months postoperatively: K apex, K2, corneal thickness at thinnest location, anterior and posterior elevation points, BCVA and UCVA. Placido –Scheimpflug topography (Sirius) device had been used to monitor the corneal parameters of the study. One –way ANOVA and Paired sample T test was used for statistical analysis.The study done in Lasik specialty center /Baghdad/IraqResults: At 1 year, an averages flattening of (2.11 D) diopter in K2 and (1.88 D) diopter in K apex were found. Mean BCVA improved by 1 line from (0.18) Log MAR to (0.13) Log MAR and mean UCVA improved by 3.5 lines from (0.89) to (0.64) log MAR. The corneal thickness at thinnest location was 5.71 Mm less than the baseline. All the above mentioned parameters showed a trend of worsening between the baseline and 1 month, and improvement thereafter. We found no statistically significant changes in the anterior elevation points while the posterior elevation point changed (increased) significantly.Conclusions: Corneal collagen cross-linking seems to be effective in decreasing progression of KC , with improvements in optical measures in many patients. Post operative parameters discussed within this review followed a seemingly reproducible trend in there natural course over 12 months .Generally, the trend that observed was immediate worsening between baseline and 1 month resolution at approximately 3 months, and improvement thereafter.
Akaike’s Information Criterion (AIC) is a popular method for estimation the number of sources impinging on an array of sensors, which is a problem of great interest in several applications. The performance of AIC degrades under low Signal-to-Noise Ratio (SNR). This paper is concerned with the development and application of quadrature mirror filters (QMF) for improving the performance of AIC. A new system is proposed to estimate the number of sources by applying AIC to the outputs of filter bank consisting quadrature mirror filters (QMF). The proposed system can estimate the number of sources under low signal-to-noise ratio (SNR).
In this paper a new structure for the AVR of the power system exciter is proposed and designed using digital-based LQR. With two weighting matrices R and Q, this method produces an optimal regulator that is used to generate the feedback control law. These matrices are called state and control weighting matrices and are used to balance between the relative importance of the input and the states in the cost function that is being optimized. A sample power system composed of single machine connected to an infinite- bus bar (SMIB) with both a conventional and a proposed Digital AVR (DAVR) is simulated. Evaluation results show that the DAVR damps well the oscillations of the terminal voltage and presents a faster respo
... Show MoreIn this paper, a new analytical method is introduced to find the general solution of linear partial differential equations. In this method, each Laplace transform (LT) and Sumudu transform (ST) is used independently along with canonical coordinates. The strength of this method is that it is easy to implement and does not require initial conditions.
Diesel engine oil was subjected to thermal oxidization (TO) for six periods of time (0 h, 24 h, 48 h, 72 h, 96 h, and 120 h) and was subsequently characterized by terahertz time domain spectroscopy (THz-TDS). The THz refractive index generally increased with oxidation time. The measurement method illustrated the potential of THz-TDS when a fixed setup with a single cuvette is used. A future miniaturized setup installed in an engine would be an example of a fixed setup. For the refractive index, there were highly significant differences among the oxidation times across most of the 0.3–1.7 THz range.
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BACKGROUND: Helicobacter pylori is an important gastrointestinal infective bacteria with many serious complications including gastric erosions and ulceration, duodenal ulcer, gastric carcinoma and MALT gastric lymphoma. The gastric biopsy is commonly performed in H. pylori-positive dyspeptic individuals, and many previous researchers studied the histopathological features of infected gastric biopsies however little previous studies focused on the histopathological findings in young population in comparison to the older one. AIM: To make a focus on the histopathological effects of H. pylori infection in young patients compared with the older one and predicts the need for endoscopy in this population, also to estimates the prevalence of
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Image compression is a serious issue in computer storage and transmission, that simply makes efficient use of redundancy embedded within an image itself; in addition, it may exploit human vision or perception limitations to reduce the imperceivable information Polynomial coding is a modern image compression technique based on modelling concept to remove the spatial redundancy embedded within the image effectively that composed of two parts, the mathematical model and the residual. In this paper, two stages proposed technqies adopted, that starts by utilizing the lossy predictor model along with multiresolution base and thresholding techniques corresponding to first stage. Latter by incorporating the near lossless com
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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