Judul: Aktivitas Fraksi Polifenol Buah Delima (Punica granatum L.) terhadap Peroksidasi Lipid Darah Tikus yang Diinduksi Parasetamol
Abstrak: Pomegranate (Punica granatum L.) has been reported to have the higher antioxidant activities than other medicinal plants. The aim of this study was to investigate polyphenol fraction activities from pulp pomegranate against blood lipid peroxidation (TBARS) in rats induced by paracetamol. Twenty two rats were divided into 7 groups. One normal group induced aquades and six treatment groups induced paracetamol (500 mg/KgBW) during 34 days. Positive control group of treatment induced Cursil®70 (13.3 mg/KgBW), and another groups induced polyphenol fraction doses 13.3 mg/KgBW, 100 mg/KgBW, 250 mg/KgBW, and 500 mg/KgBW, respectively. Polyphenol were extracted by acetone:water:acetic acid (90:9.5:0.5). TBARS was reacted by thiobarbituric acid (TBA) 1% and analysed by spectrophotometer in λ=532 nm. Polyphenol yields have got 36.2%. Paracetamol inductions as long as 34th days increased TBARS levels 120.66%. The PC and FD13.3 decreased TBARS 8.14% and 10.45% not significantly (p=0.548). FD100, FD250, and FD500 decreased TBARS 18.18%, 33.23%, and 41.84% (p<0.05), respectively.
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Judul: Efek Tepung Delima (Punica granatum L.) sebagai Sumber Antioksidan pada Metabolisme Tikus Putih (Rattus norvegicus) yang Dikontaminasi Asap Rokok, Effect of Pomegranate Powder as Antioxidant Source on Metabolism of White Rat (Rattus norvegicus) Contaminated with Cigarette Smoke
Abstrak: The effect of pomegranate fruit and seed powder as a source of antioxidant on consumption, digestion of lipid and crude fiber are evaluated in this study. Thirty of 28 day old rats were used in this experiment. This experiment used completely randomized design with three treatments and ten replications. The experimental rats were given diets containing pomegranate at levels of 0% (R0= control diet), 5% (R1= R0 containing 5% pomegranate fruit and seed powder) and 10% (R2= R0 containing 10% pomegranate fruit and seed powder). Variables measured were ether extract, crude fiber, NFE intakes and digestibilities and abdominal circumference. Data were analyzed with Analysis of Variance (ANOVA) and differences among treatments were tested by contrast orthogonal tests. Results show treatments affected nutrient intakes (P<0.01), but did not produced significant effect on nutrient digestibilies. There were no significant effect of treatments on the change in abdominal circumference. In conclusion, the use of pomegranate fruit and seed powder increased fat and carbohydrate intake in mice contaminated with cigarette smoke; however there were no effect on fat and carbohydrate digestibilities, and the change in abdominal circumference. The best result was produced by R1 which contained 5% pomegranate fruit and seed powder.
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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