Judul: Perbandingan nilai fisiologis kardiorespirasi dan suhu tubuh pada berbagai umur anak anjing kampung
Abstrak: Pengetahuan lebih jauh mengenai nilai fisiologi anjing kampung sangat diperlukan karena meningkatnya jumlah anjing yang dijadikan hewan piara selain juga berperan sebagai hewan percobaan. Saat ini baru sedikit studi mengenai nilai fisiologis kardiorespirasi dan suhu tubuh anak anjing kampung. Penelitian ini dilakukan untuk menentukan nilai normal elektrokardiogram (EKG), frekuensi jantung, tekanan darah (sistol, diastol dan rata-rata), frekuensi pernapasan dan suhu tubuh pada berbagai umur anak anjing kampung sehat secara klinis. Pengukuran nilai fisiologis tersebut dilakukan pada tujuh ekor anak anjing kampung jantan yang berasal dari satu induk, mulai dari umur dua sampai tujuh bulan. Semua pengukuran parameter fisiologis dilakukan dengan alat fisiograf model BSM-8800 (Nihon Kohden®). Hasil penelitian ini menunjukkan terdapat perbedaan nilai EKG antara anak anjing umur dua bulan dan anak anjing umur tujuh bulan. Semakin dewasa umur anjing, nilai frekuensi pernapasan dan suhu tubuh cenderung semakin meningkat. Sebaliknya, nilai frekuensi jantung semakin menurun. Adapun nilai tekanan darah relatif tidak mengalami perubahan.
Keyword: kardiorespirasi, anak anjing

Judul: Gambaran darah anjing kampung(Canis familiaris) di daerah Jakarta dan Bogor
Abstrak: Perbedaan kondisi lingkungan dan ras dapat menyebabkan perbedaan pada gambaran darah. Penelitian ini bertujuan untuk mendapatkan gambaran darah anjing kampung (Canis familiaris) yang sehat secara klinis di daerah Jakarta dan Bogor. Tiga puluh sembilan sampel darah dikoleksi dari vena cephalica antibrachii dorsalis dengan menggunakan disposable syringes 3 ml yang telah diberi heparin. Nilai-nilai darah dihitung dengan menggunakan mesin penghitung elektronik (Hemavet). Dari hasil penelitian terhadap sampel darah anjing kampung yang sehat secara klinis diketahui bahwa anjing kampung umur (1-2) tahun memiliki kisaran nilai eritrosit, hemoglobin, hematokrit, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), leukosit, neutrofil, limfosit, eosinofil dan trombosit berturut-turut adalah 7.21±1.86x10/µl, 14.8±3.95g/dl, 45.97±12.5%, 63.96±8.47fl, 20.65±3.07pg, 32.61±4.93%, 16.37±1.37%, 15.87±4.76x103/µl, 8.02±3.61x103/µl, 7.62±2.96x10³/µl, 0.13±0.14x10³/µl dan 193.42±70.1x10³/µl. Sedangkan anjing kampung berumur lebih dari 2 tahun memiliki nilai eritrosit, hemoglobin, hematokrit, MCV, MCH, MCHC, RDW, leukosit, neutrofil, limfosit, eosinofil dan trombosit berturut-turut adalah 8.87±2.03x10/µl. 18.41±4.76g/dl, 53.55±11.51%, 61.42±9.21fl, 20.68±2.04pg. 34.6±7.11%, 16.33±1.18%, 12.33±3.54x103/µl, 6.47±2.38x103/µl, 5.77±3.17x103/µl, 0.08±0.12x10³/µl dan 222.8±105.82x103/µl.
Keyword: 

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