Judul: Pengaruh Pemberian Ekstrak Tumbuhan Obat Antimalaria  Quassia indica Terhadap Toksikopatologi Organ Hati dan  Ginjal Mencit (Mus musculus).
Abstrak: The aim of this research was to study the toxicopathology effect of Quassia indica act of distributing with various level doses in the liver and kidney of mice (Mus musculus). 25 mice (Mus musculus) separated into 5 groups which were adapted for two weeks before using in this experiment. Four groups were received Quassia indica extract at various level doses, of 1, 10, 100, and 1000 mg/kg body weight, and one group of mice acted as control which received with Carboxymethocellulose (CMC-Na). After receiving Quassia indica for seven days the mice then were euthanized with over dose of ether. The liver and kidney were taken and samples fixated in Buffer Neutral Formalin 10% solution for 48 hours then the making of histopathology slides. Histopathology slides stained with Haematoksilin Eosin and observed with video-photo microscope. In the liver, observation was founded the cell with degeneration and apoptotic around vena porta and vena centralis, and in the kidney was founded degeneration and apoptotic of tubule epithelium, glomerular atrophy and protein sedimentation. Based on the result and analized with statistic method can be concluded that the distribution of Quassia indica extract with 1000 mg/kg BW doses could emerge a significant lesion on the liver and kidney of mice (Mus musculus).
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Judul: Pemeriksaan Toksikopatologi Efek Pemberian Berbagai Fraksinasi Ekstrak Batang Gatep Pahit (Quassia indica (Gaernt.) Nooteboom) Pada Organ Hati dan Ginjal Mencit (Mus musculus)
Abstrak: The aim of this research was to study the toxicopathology effect of gatep pahit (Q. indica) stem extract fractionation given using various solvents of different polarity (hexane, chloroform, ethyl acetate, and ethanol residue) on mice (Mus musculus) liver and kidney. Twenty five mice (Mus musculus) were divided into five groups. Four groups were given 100 mg/kg BW stem extract fractionation of Q. indica using various different solvent and one group of mice performed as control which received carboxymethocellulose (CMC-Na) 1%. After 1 week of treatment, the mice then were euthanized with over dose of ether. The liver and kidney were taken and fixed in 10% of Buffer Neutral Formalin for 48 hours. The tissue samples were processed to make histopathology slides stained with Hematoxylin Eosin. The tissues were examined under the light microscope. The toxicopathology assay included to count the cells under went necrosis/apoptosis and degeneration. The results revealed that the Q. indica chloroform fraction has the lowest level of damage to hepatocytes while hexane fraction showed the most hepatocytes damaged. The Q. indica hexane fraction caused the less damage in kidney tubular epithelium while the ethyl acetate showed the most epithelium damaged.
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Judul: The Construction of Greedy SVP LLL Algorithm
Abstrak: A lattice is a set of all integer linear combination of a set of linearly independent vectors in ℝ𝑛���. The independent vectors are called bases of lattice. Any lattice can be generated from many bases, and these bases have the same cardinality. In the lattice, the most fundamental and renowned problem is the Shortest Vector Problem (SVP). SVP is a tracking problem of the shortest nonzero vector in a lattice with equivalent bases. In two dimensions, SVP problem has resolved exactly by Gauss’ algorithm. When the lattice dimension is higher than two, A.K. Lenstra, H.W. Lenstra and L. Lovasz gave a LLL algorithm to compute approximation of nonzero shortest vector in the lattice. Reduced bases solution obtained from LLL algorithm still be an approximation and has polynomial running time of arbitrary dimension which large enough. In 1994, Schnoor and Euchner modified this LLL algorithm as a new variant which later was named deep insertion LLL algorithm. This algorithm is a modified version of the LLL algorithm to the terms of the exchange step to improve the accuracy of output of LLL reduced bases and applying it to the subset sum problem. The purpose of this research are: (1) Reconstruct the LLL algorithm and deep insertion LLL algorithm using a geometric approach. (2) Construct a greedy SVP LLL algorithm. (3) Implement the three algorithms in a symbolic programming language, and evaluate them experimentally. A bases ℬ=[𝐛���1,𝐛���2,…,𝐛���𝑛���] in ℝ𝑚��� is called reduced LLL with parameter 𝛿��� if satisfies: (a) |𝜇���𝑗���𝑖���|≤12, for every integer 𝑖���,𝑗��� with 1≤𝑖���<𝑗���<𝑛���. (b)𝛿���‖𝜋���𝑗���(𝐛���𝑗���)‖2≤‖𝜋���𝑗���(𝐛���𝑗���+1)‖2, for 𝑗���=1,2,…,𝑛���−1,where 𝛿��� is a reduced parameter of real numbers with 14<𝛿���<1. The first requirement is the reduced basis 𝛿��� must “nearly orthogonal” and in its computation case, this requirement can be reached out by using the Gram-Schmidt’s orthogonalization. While the second requirement is called exchange step, or used to called as Lovasz condition. By using both of these requirements, then they establish the LLL algorithm. Then, Schnoor and Euchner modify the LLL algorithm based on the requirements of the exchange step called the deep insertion LLL algorithm, where the exchange step is based on the comparison of the projected vector in orthogonal complement [𝐛���1,𝐛���2,…,𝐛���𝑘���−1]⊥ after j-th reduction. To construct greedy SVP LLL algorithm, the requirements of the exchange step is based on purely by comparing norm of lattice 𝐛���𝑗��� with norm lattice 𝐛���𝑖��� for 𝑖���=1,2,3…,𝑗���−1. The process of insertion reduced vector after the exchange step also has done greedily. Then, we calculate the number of arithmetic operation in greedy SVP LLL algorithm for further analysis. The result of our exprimentation for three algorithms on the certain matrix size shows that the running time of the three algorithms increases propotional to the size of the matrix. The result also shows that by using 𝛿���=34 for the LLL algorithm and the deep insertion LLL algorithm, and the greedy SVP LLL algorithm which is a new variant made by using no parameter of 𝛿���, outperform of the other of two previous algorithm in terms of speed with the same output.
Keyword: LLL algorithm, LLL deep insertion algorithm, greedy SVP LLL algorithm