Judul: Non-Invasive Hormone Monitoring: Faecal Androgen and Glucocorticoid in Male Crested Macaques (Macaca nigra) in Relation to Seasonal and Social Factors.
Abstrak: Monitoring hormon secara non-invasiv menggunakan sampel feses merupakan teknik yang semakin populer. Hal ini didasarkan pada prinsip bahwa selain dari darah, hormon juga bisa diukur dari feses, urin dan saliva. Feses mengandung hormon steroid seperti progesteron, estrogen, androgen dan glukokortikoid dalam bentuk metabolit. Meskipun koleksi sampel feses relatif mudah, tetapi analisis hormon tidaklah mudah. Oleh karena itu, uji validasi untuk menentukan jenis metabolit hormon merupakan hal yang perlu dan penting untuk dilakukan, sehingga akan diperoleh hasil yang valid. Teknik non-invasiv ini memberikan kemudahan bagi kita untuk mempelajari fisiologi dari hewan liar atau hewan terancam punah (endangered) seperti monyet hitam Sulawesi (Macaca nigra) dari habitat alaminya. Monyet hitam Sulawesi adalah monyet endemik dari Sulawesi dengan status kritis (critically endangered animal). Namun, informasi tentang biologi reproduksi, fisiologi dan tingkah laku pada monyet ini masih sangat terbatas.
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Judul: Perilaku Harian Monyet Hitam Sulawesi (Macaca nigra) pada Masa Kebuntingan di Cagar Alam Tangkoko-Batuangus, Sulawesi Utara
Abstrak: Crested macaque (Macaca nigra), one of the seven endemic primates in Sulawesi is the most endangered macaque species that has been categorized as critically endangered by International Union for Conservation of Nature (IUCN). However, almost nothing is known about the reproductive biology in associated with the behavioral during pregnancy. The data is important to support a management and breeding programs such as pregnancy diagnostic and conservation program in this species. The research aimed to study the behavior of the crested macaques during pregnancy. Observations were performed on the 10 adult females of the Rambo 1 group (R1) which has been habituated at Tangkoko-Batuangus Nature Reserve, North Sulawesi. Behavioral data were collected using focal animal sampling and recorded using instantanaeous sampling on females during menstrual cycling, pregnancy, and after parturition (lactation). During five months observations, a total of 393,63 observation hours were recorded. The results showed that daily activities of the M. nigra during menstrual cycling were dominated by feeding (26.60%), foraging (15.95%), and locomotion (16.16%). Daily activities during pregnancy were dominated by feeding (27.03%), foraging (16.37%), and resting (13.45%), whereas after parturition (lactation) period was dominated by resting (26.12%), foraging (19.51%) and feeding (14.98%). The resting was the most activity in the end of pregnancy and few day after parturition. The diets which were collected successfully composed of the fruits (34 species, 20 families), arthropods (3 species, 2 families, 4 orders), fungi (4 genera and division), and plants (5 species and families). Climate influenced on the composition of the diet, especially on fruit and arthropods. In conclusion, the activity budgets were different during menstrual cycling, pregnancy, and lactation, especially feeding, foraging, locomotion, and resting.
Keyword: Bogor Agricultural University (IPB), diet, lactation, pregnancy, menstrual cycling, M. nigra

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