It is written to discuss the effect and syndromes of novel YL-1 hollow needle aspiration drainage system to treat chronic subdural hematoma. Collecting clinical data about 697 patients with chronic subdural hematoma in neurosurgery of People' Hospital in North Jiangsu from January 2004 to December 2014, including clinical manifestation, imaging data, operation time, postoperative complications and prognostic factors and so on. 593 patients got cured, 53 patients with recurrence, 19 patients with acute subdural hematoma, 13 patients with poor drainage, 9 case of patients with acute epidural hematoma, puncture failure in 6 cases, 3 cases of pulmonary infection, one got intracranial hemorrhage (brain stem and basal ganglia hemorrhage). The total time of the operation is 15-28 min, the mean time is 18 ± 3.6 min, the average retention time of novel YL-1 hollow needle aspiration drainage system was 2.6 ± 1.3 days, the average use of urokinase was 30,000 ± 2.10,000 units. It takes a short time for novel YL-1 hollow needle aspiration drainage system to treat chronic subdural hematoma without any syndromes like brain tissue injury, tension pneumocrania, intracranial infection and so on. The clinical cure rate is 85.08 %, recurrence rate is 7.6 %. Using novel YL-1 hollow needle aspiration drainage system to treat chronic subdural hematoma is such a minimally invasive surgical technology which has a higher curative rate, small damage, is also easy to operate with security and less severe complications.
The singular value decomposition (SVD) has many real-time applications. Recently, there has been much interest in developing efficient methods to compute SVD in parallel machines. This paper presents an efficient method for computing SVD in a cube connected SIMD (single instruction stream -multiple data stream) parallel computer. The method is based on a one-sided orthogonalization algorithm due to Hestenes. In a cube connected SIMD with n/2 processors, the SVD of an m by n matrix requires a computation time of O(mn) per sweep. Although the time: complexity (excluding communication time) is the same as that of the best known SVD method on linearly connected SIMD, the communication time is much smaller because the amount of data moved among tbe nodes is only about one half. The SVD of large matrices on a fixed size system is also discussed,
IntroductionA singular value decomposition (SVD) of an m by n (m 2 n) matrix A is its factorization into the product of three matrices: A = UDV= (1.1) where U is an m by R matrix with orthogonal columns, D is an n by n non-negative diagonal matrix, and the n by n matrix V is orthogonal. The n elements of D are called the singular values of matrix A. The singular value decomposition (SVD) has many applications', for some of which real-time computation is required. It is perhaps the most important factorization of a real m x n (m 2n ) matrix.Recently, there has been much interest in develop ing parallel SVD algorithms for various types of computer and in designing algorithmically specialized VLSI afiays such as the systolic arrays to compute SVse7. Particularly notable are the method by Brent and Luk for an n /'Z-node linear array with 0 (Smn ) time complexity2 and the method by Brent, Luk, and Van Loan 3 for an n2/4-node mesh array with time complexity of 0 (m + Sn), where S is the number of sweeps.In this paper we present an efficient method for computing the SVD on a cube connected SIMD machine. Like the method by Brent and Luk for linear array, our
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