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: Dinamika profil hematologi dan rasio netrofil: limfosit monyet ekor panjang (Macaca fascicularis) pada pengaturan mikroklimat ruangan
Abstrak: Penelitian ini bertujuan untuk mengkaji nilai fisiologis hematologi (eritrosit, PCV, Hb, MCV, MCH, MCHC, dan leukosit) dan rasio netrofi:limfosit monyet ekor panjang pada kondisi dinamis dengan perbedaan pengaturan mikroklimat (suhu dan kelembaban). Penelitian ini juga bertujuan untuk mengkaji kemungkinan terjadinya gejala stres melalui pengukuran rasio netrofil:limfosit sebagai indikator stres secara tidak langsung. Pada penelitian ini digunakan 10 monyet ekor panjang jantan berumur 3 hingga 5 tahun yang ditempatkan pada kamar dengan suhu dan kelembaban ruangan sebesar 29,00±1,95°C dan 79,52±1,57% selama 7 hari untuk masa adaptasi. Setelah itu, dilakukan pengaturan suhu dan kelembaban ruangan sebesar 25,79±1,16°C dan 80,19±9,05% selama 14 hari pada masa perlakuan. Pada perlakuan akhir, monyetmonyet ini dikondisikan kembali pada suhu dan kelembaban ruangan sebesar 29,00±1,95°C dan 79,52±1,57% selama 14 hari pada masa pasca perlakuan. Pengambilan darah dilakukan melalui vena femoralis pada hari terakhir masa adaptasi, hari ke-1, ke-4, ke-7, ke-14, dan ke-28. Hasil yang diperoleh menunjukkan bahwa tidak ada perbedaan yang signifikan dari pengaturan suhu dan kelembaban ruangan terhadap kondisi hematologi dan rasio netrofil:limfosit. Hal ini dapat dibuktikan dengan nilai seluruh parameter yang diperoleh masih berada dalam rentang nilai normal sesuai literatur. Selain itu, tidak ditemukan adanya gejala stres berdasarkan kajian rasio netrofil:limfosit. Kondisi dengan suhu dan kelembaban ruangan sebesar 25,79±1,16°C dan 80,19±9,05% merupakan kondisi terbaik bagi kehidupan monyet ekor panjang.
Keyword: Biomedical, Hematology, Long-tailed Macaque, Microclimate, Neutrophil, Lymphocyte Ratio, Bogor Agricultural University

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