Judul: Respon Stres Dengan Penanda Kortisol &lam Urine Monyet Ekor Panjang (Macoca fnscicularis) setelah Mengalami Transportmi
Abstrak: Stress during tranqxtaticm is one of the primary ocm*ing facia in eqmhng non human primate to the target country- Cartid mdabdite rate measuclements of urine as one of the @es makers were mwhctd using ELISA on 30 adult f d e M-Q fuscdarLs 0 after 24 hours of tmqatation. The aims of tbe research were to investigate the mpnse indud by tmmsp&atioa, using windowed cage aad closed, and &&rent &ansgortatiw fed rations. The result showed h t there was a differearn in he existeoce of urine cortisol levels of Mf at the first 8 hours, which decrease until reaching normal rate after 24 hours transportation (P<0.05). The animals which were placed in windowed cages had a lower corliso! levels thao those in the clod cages. This indicated that the animals in window cages had a lower response to stress. It did not have any differences (M.05) on the five different fd rations bring transportation. The results of this research indicated that window cages were more appropriate compred ta dosed cages.
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Judul: Biological study of longtailed macaques (macaca fascicularis) which experience transportation
Abstrak: During transportation of monkeys, environment conditions change, cause stress to the animals and consequently the rate of body metabolism increases. This condition influences the stability of biological and physiological activities of the animals, and could cause death. It is therefore necessary to apply appropriate management during transportation of the monkeys. This study was conducted in Bogor and Kuningan counties in two periods, 26 days in September 2003 for transportation time of eight hours, and another 28 days in October – November 2003 for transportation time of 24 hours. The transportation times were treatments applied in the experiment, in which 30 adult female monkeys of 2.5-3.5 kgs of weight were used for each treatment. In addition, five types of rations were given during transportation, and the monkeys were put in two types of individual cages, with and without windows, to observe the effect of open and closed cages. A complete randomized design, arranged factorially was used. Total feed consumption, coefficient of digestibility, changes in body weight, and behaviour of animals during and after transportation were measured in this study. The results of the study indicated that transportation caused changes in almost all aspects of the treatments but, there were no significant differences found due to the type of transportation cages used. Based on the recovery times needed to get back to normal non-stressed behaviour, it was found that longer transportation periods (24 hours vs eight hours) needed longer recovery times. It is recommended that rations during transportation have high levels of protein (monkey chow), and be supplemented with appropriate fruits to furnish the raw fiber and high palatibility.
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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