Abstract ABSTRACT:BACKGROUND: Anterior cruciate ligament reconstruction (ACLR) is one of the most commonly performed orthopedic procedures. Technical factors especially correct tunnel placement play major role in its success. However its failure rate is still high (10%), and impingement of the graft on the posterior cruciate ligament (PCL) and the medial wall of the lateral femoral condyle is an important cause of failure. Wallplasty is a technique used to prevent graft impingement, but there is no consensus on its routine use.OBJECTIVE:Is to compare between the postoperative knee functional outcome and stability of arthroscopic ACLR performed with wallplasty versus those performed without wallplasty.PATIENTS AND METHODS: A prospective experimental non randomized study was performed on 32 patients (30 males and 2 females) who necessitated arthroscopic ACLR. The patients were divided into 2 groups, in group A (made of 16 patients) the reconstructions were done without wallplasty and in group B (made of 16 patients) were done with wallplasty. Three months postoperatively the two groups were compared in regard to Lasholm score changes (preoperative and postoperative), Lachman test, and Pivot shift test results.RESULTS: There was better improvement in Lasholm score in group B than in group A, and the difference was statistically significant (p value =0.036). Knee stability tests were better in group B than in group A, but the differences were statistically not significant.CONCLUSION: Wallplasty has statistically better functional outcome than non wallplasty in ACLR and it is recommended to be done routinely in all cases of ACLR.
Crystalline silicon (c-Si) has low optical absorption due to its high surface reflection of incident light. Nanotexturing of c-Si which produces black silicon (b-Si) offers a promising solution. In this work, effect of H2O2 concentrations towards surface morphological and optical properties of b-Si fabricated by two-step silver-assisted wet chemical etching (Ag-based two-step MACE) for potential photovoltaic (PV) applications is presented. The method involves a 30 s deposition of silver nanoparticles (Ag NPs) in an aqueous solution of AgNO3:HF (5:6) and an optimized etching in HF:H2O2:DI H2O solution under 0.62 M, 1.85 M, 2.47 M, and 3.7 M concentrations of H2O<
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Necessary and sufficient conditions for the operator equation I AXAX n*, to have a real positive definite solution X are given. Based on these conditions, some properties of the operator A as well as relation between the solutions X andAare given.
This paper studies a novel technique based on the use of two effective methods like modified Laplace- variational method (MLVIM) and a new Variational method (MVIM)to solve PDEs with variable coefficients. The current modification for the (MLVIM) is based on coupling of the Variational method (VIM) and Laplace- method (LT). In our proposal there is no need to calculate Lagrange multiplier. We applied Laplace method to the problem .Furthermore, the nonlinear terms for this problem is solved using homotopy method (HPM). Some examples are taken to compare results between two methods and to verify the reliability of our present methods.
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Non-thermal argon plasma needle at atmospheric pressure was
constructed. The experimental setup was based on a simple and low
cost electric component that generates a sufficiently high electric
field at the electrodes to ionize the argon gas which flow at
atmospheric pressure. A high AC power supply was used with 1.1
kV and 19.57 kHz. Non-thermal Argon plasma used on blood
samples to show the ability of non-thermal plasma to promote blood
coagulation. Three tests have been done to show the ability of plasma
to coagulate both normal and anti-coagulant blood. Each blood
sample has been treated for varying time from 20sec. to 180sec. at
different distances. The results of the current study showed that the
co
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In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual output of the system & to find the jacobain of the system. Which appears to be of critical importance parameter as it is used for the feedback controller and the second stage is learned on-line to modify the weights of the model in order to control the variable parameters that will occur to the system. A back propagation neural network is appl
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