Judul: Isolasi karakterisasi bakteri metanotrof asal sawah di Bogor dan Sukabumi
Abstrak: Sebesar 43% dari emisi metan (CH,) ke ahnosfer berasal dari lahan basah dan sawah. Peningkatan emisi satu molekul CH, berpotensi sebagai gas mmah kaca 20 kali lebih besar daripada satu molekul C02. Sebagian besar emisi CH4 di kawasan sawah yang tergenang diproduksi oleh bakteri metanogen dan sebanyak 80 % dari CH4 tersebut dioksidasi oleh bakteri metanotrof. Bakteri metanohof memiliki sistem enzim yang spesifik yaitu metan monooksigenase (MMO) yang mampu menambahkan satu atom oksigen ke dalam molekul CH4 untuk membentuk senyawa metanol. Aplikasi bakteri metanohof dapat mengurangi emisi metan dari lahan sawah. Ekplorasi bakteri metanohof dari contoh sedimen asal sawah di Bogor dan Sukabumi yang ditumbuhkan pada media Nitrat Mineral Salts (NMS) menghasilkan 40 isolat. Aktivitas oksidasi CH, diukur dengan menggunakan kromatografi gas, sedangkan aktivitas soluble rnethane monooxygenase (sMMO) diuji dengan metode kolorimehik. Enam isolat (BGM 1, BGM 2, BGM 3, BGM 12, SKM 14, BGM 9) dikarakterisasi aktivitas oksidasi metannya secara berkala. Aktivitas oksidasi CH4 tertinggi pada isolat BGM 9, yaitu sebesar 66556,82 moll hari. Aktivitas enzim sMMO teridentifikasi pada isolat BGM 9.
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Judul: Karakterisasi Fisiologi dan Identifikasi Molekuler Isolat-isolat Bakteri Metanotrof Asal Sawah Wilayah Bogor dan Sukabumi
Abstrak: Methane (CH4) is one of the main greenhouse gasses that could absorb infra-red radiation so it contribute to global warming. Methanotrophs are bacteria that are able to grow using methane as their only source of carbon and energy. They play a major role in the reduction of the release of methane into the atmosphere from environments. Methanotrophic bacteria isolated from Ricefields in Bogor and Sukabumi (BGM 1, BGM 2, BGM 3, BGM 9, and SKM 14) had a high methane oxidation. Activity physiologycal assay using API 20 NE and molecular identification using 16S rRNA gene sequence analysis were conducted in this research. Five isolates showed a positive reaction of physiological assays for gelatine, β-galactosidase enzyme, glucose assimilation, mannitol assimilation, N-asetil-glukosamin assimilation, maltose assimilation, and malic acid assimilation. BGM 1 and BGM 3 16S rRNA sequence showed that they had similarity to Methylocystis rosea into 74% and 70% respectively, while BGM 2 and BGM 9 had similarity to Methylococcus capsulatu into 83% and 85% respectively, and SKM 14 had 65% similarity with Methylobacter sp. Phylogenetic tree showed that BGM 1 and BGM 3 isolates were in the same group with Methylocystis rosea strain SV97T, Methylobacterium extorquens, and Methylosinus trichosporium, nevertheless they were very close related to Methylocystis rosea strain SV97T. BGM 2, BGM 9, and SKM 14 isolates were in the same group with Methylococcus capsulatus strain texas, Methylomicrobium buryatense, and Methylobacter sp. nevertheless BGM 2 and BGM 9 isolates were very close related to Methylococcus capsulatus strain texas. While SKM 14 was very close related with Methylobacter sp.
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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