Judul: Keracunan Merkuri (Hg) pada Unggas
Abstrak: Tujuan penulisan ini adalah mengumpulkan pengetahuan tentang keracunan Merkuri ( Hg ) pada unggas, yang kebanyakan disebabkan oleh karena pencemaran yang terjadi pada lingkungan. Bahan penulisan ini diperoleh dari kepustakaan yang tersedia. Topik pembicaraan mencakup sejarah keracunan merkuri ( Hg ); etiologi yang mencakup ·sumber merkuri sebagai penyebab keracunan, merkuri di dalam tubuh, toksisitas merkuri; pathogenesa; gejala klinis, pathologi.klinis, pasca mati keracunan merkuri pad a unggas; penanggulangan keracunan merkuri pada unggas; diagnosa dan diagnosa banding terhadap keracunan merkuri.
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Judul: Reduksi Kadar Merkuri Pada Kerang Hijau (Mytillus viridis) Di Cilincing Jak Serta Pemanfaatannya Dalam Produk Kerupukarta Melalui Metode Asam
Abstrak: Masalah logam berat semakin banyak mendapat perhatian masyarakat. Namun yang selalu menjadi topik pembicaraan masyarakat adalah merkuri (Hg), hal ini mungkin disebabkan oleh rasa ketakutan masyarakat terhadap keganasan sifat racun merkuri, seperti yang pemah teIjadi di Teluk Minamata. Keracunan logam berat merkuri diawali pada tahun 1953 di Teluk Minamata, Jepang yang mengakibatkan banyak mahluk hidup air mati bahkan korban manusia dan bayi yang lahir cacat mental. Sedangkan di Indonesia pencemaran merkuri mulai terlihat pada tahun 1993 di perairan Kenjeran, Pantai Timur Surabaya, kandungan merkuri pada lumpur sebesar 0,60 ppm dan pada tahun 1996 darah dari sampel warga Kenjeran mengandung merkuri 2,48 ppb. Kandungan merkuri pada Waduk Saguling, Bandung mencapai 0,06 mg/L melebihi dari batas normal 0,002 mg/L. Selain itu, pada tahun 2004 daerah aliran sungai (DAS) Kapuas Kalimantan Barat di Nanga Sepauk mengandung 32,20 ppm merkuri.
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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