Background: the condition of hallux valgus is considered as the most common deformities affecting females more than males, characteristically manifested as lateral deviation of the big toe and widening of first and second inter -metatarsal angle with a deformity of second toe in some severe cases. Objective: to make a radiological and clinical assessment of two surgical methods of osteotomy used in treatment of hallux valgu and to compare between them: first one is the distal dome osteotomy, and second one is a distal wedge metatarsal osteotomy. Patients and methods: a total of 36 feet of 28 patients suffer from hallux valgus, with mean age of 50.3 years were included in this study, followed for 6- 30 months ( mean follow-up of 8.8 months). Nineteen feet treated by dome osteotomy and seventeen feet treated by wedge osteotomy. All the cases were evaluated by the american orthopedics foot and ankle society (aofas) score, also, through the hallux valgus angle and intermetatarsal angle, both before and after surgery. Results: by dome osteotomy, the preoperative mean result of aofas score was about 45.7, with hallux valgus angle (hva) of 33.2o and intermetatarsal angle (IMA) of 11.7º. Postoperatively, the mean result of AOFAS score was 82.8, with HVA of 14.3º and IMA of 7º, with about 94.7% satisfactory results. In the other hand, the method of wedge osteotomy showed a preoperative mean result for AOFAS score of 45.2, with HVA of 34º and IMA of 12.8º , compared with postoperative mean result of AOFAS score of about 80.7, with HVA of 15.8º and IMA of 7.7º, with about 82.8 % satisfactory results. Conclusions: the two methods of osteotomy were used with very good outcome in radiological and clinical treatment of hallux valgus. :
Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an
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The use of non-parametric models and subsequent estimation methods requires that many of the initial conditions that must be met to represent those models of society under study are appropriate, prompting researchers to look for more flexible models, which are represented by non-parametric models
In this study, the most important and most widespread estimations of the estimation of the nonlinear regression function were investigated using Nadaraya-Watson and Regression Local Ploynomial, which are one of the types of non-linear
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Transport layer is responsible for delivering data to the appropriate application process on the host computers. The two most popular transport layer protocols are Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). TCP is considered one of the most important protocols in the Internet. UDP is a minimal message-oriented Transport Layer protocol. In this paper we have compared the performance of TCP and UDP on the wired network. Network Simulator (NS2) has been used for performance Comparison since it is preferred by the networking research community. Constant bit rate (CBR) traffic used for both TCP and UDP protocols.
In this study, we review the ARIMA (p, d, q), the EWMA and the DLM (dynamic linear moodelling) procedures in brief in order to accomdate the ac(autocorrelation) structure of data .We consider the recursive estimation and prediction algorithms based on Bayes and KF (Kalman filtering) techniques for correlated observations.We investigate the effect on the MSE of these procedures and compare them using generated data.
Through recent years many researchers have developed methods to estimate the self-similarity and long memory parameter that is best known as the Hurst parameter. In this paper, we set a comparison between nine different methods. Most of them use the deviations slope to find an estimate for the Hurst parameter like Rescaled range (R/S), Aggregate Variance (AV), and Absolute moments (AM), and some depend on filtration technique like Discrete Variations (DV), Variance versus level using wavelets (VVL) and Second-order discrete derivative using wavelets (SODDW) were the comparison set by a simulation study to find the most efficient method through MASE. The results of simulation experiments were shown that the performance of the meth
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Abstract
The methods of the Principal Components and Partial Least Squares can be regard very important methods in the regression analysis, whe
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Regression models are one of the most important models used in modern studies, especially research and health studies because of the important results they achieve. Two regression models were used: Poisson Regression Model and Conway-Max Well- Poisson), where this study aimed to make a comparison between the two models and choose the best one between them using the simulation method and at different sample sizes (n = 25,50,100) and with repetitions (r = 1000). The Matlab program was adopted.) to conduct a simulation experiment, where the results showed the superiority of the Poisson model through the mean square error criterion (MSE) and also through the Akaiki criterion (AIC) for the same distribution.
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