Judul: Profil Eritrosit Anjing yang Terinfeksi Kronis Babesia sp.
Abstrak: Babesiosis is an important disease in dogs caused by Babesia sp, that it was caused anemia. The purpose of this research was to obtain the profile of dogs erythrocyte which chronic infected of Babesia sp. This research used thirteen hound dogs from the K-9 Unit of Direktorat Polisi Satwa, Kelapa Dua, Depok regardless of breed, age, and sex. The blood samples as much as 3 ml were taken through Cephalic vein. Blood smears were made for the parasitemia percentage calculation. The count of erythrocytes, hemoglobin concentration, and percentages of hematocrit were analyzed by cell counter-blood analyzer Hemavet®. The results showed that the percentages of parasitemia, erythrocytes count, hemoglobin concentration, and value of hematocrit were (0.53±0.35)%, (5.38±0.44)x106/μl, (13.19±1.43) g/dl, and (36.62±3.45)% respectively. The conclusions showed that the parasitemia percentages were mild infection (1%). The erythrocytes count, hemoglobin concentration, and the value of hematocrit were in the low normal range.
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Judul: Profil Hematologi dan Kimia Darah Anjing yang Terinfeksi Kombinasi Babesia sp. dan Haemobartonella sp. Kronis
Abstrak: The objective of this experiment was to study the hematology and blood chemistry profiles of dog infected chronically by Babesia sp. and Haemobartonella sp. combination. Twenty eight dogs 2-8 years of age positively infected the combination of Babesia sp. and Haemobartonella sp. were devided into five groups depending on breeds, i.e. BM/Belgian Malinois (n= 4), GR/Golden Retriever (n=3), LR/Labrador Retriever (n=6), GS/German Shepherd (n=6, and RW/Rotweiller (n=9). Physical examination were done to obtain clinical findings. Blood samples were collected from cephalica antibrachii vein to determine the degree of infection, routine hematology (erythrocyte count, hemoglobin concentration, hematocrit, leukocyte count, neutrophyl, eosinophyl, basophyl, limphocyte, monocyte, and thrombocyte count) and clinical chemistry (ALT and AST activity, bilirubin count, conjugated bilirubin, unconjugated bilirubin, protein count, ureum, creatinine, sodium, potassium, pH, pCO2, HCO3, pO2 and sO2). Results of this study indicated that the degree of infection of Babesia sp. dan Haemobartonella sp. were < 1 % at all breed of dogs. Clinical findings were various including pale mucous membrane, hiperemic conjunctiva and sclera, arythmia, bradycardy, tachycardy, splenomegaly, bloody diarrhea, and ticks infestation. The erythrocyte count, hemoglobin concentration, hematocrit, and thrombocyte counts tend to be lower than normal range at all groups. The other findings such as total bilirubin, conjugated bilirubin, ureum, and total protein tend to be higher than normal range, while oxygen tension (pO2) and oxygen saturation (sO2) tend to be lower than normal range at all breed of dogs..
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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