Judul: Metabolism of Date Extract in Dengue Hemorrhagic Fever Patients: Hematological Studies.
Abstrak: Dengue Hemorrhagic Fever (DHF) is one of health problems in Indonesia. The purpose of this research was to look for an alternative medication for DHF. This research was conducted by giving date extract to 14 DHF patients. Dose of date extract used was 30 mL per day. As a control for comparison, this research used medical records data of 9 patients. All of the patients were men, with age ranging from 15 to 35 years old. The blood overview measured were platelet, hematocrit, hemoglobin, and leukocyte using automatic blood counters Technicon H-1. In this research, hospital treatments were still executed. The treatments included giving medicines and adding ringer lactate infuses. The result of this research showed that the daily platelet increase percentage of DHF patient by giving date extract was higher than that of control data. The mean of platelet increase percentage per day by giving date extract was 23,90%, while the mean of the control data was 8,09%. The overall ANOVA analysis of the patients blood overview showed that giving date extract to DHF patients was not statistically significant (p>0,005).
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Judul: Kajian Potensi Transmisi Penyakit Demam Berdarah Dengue Berdasarkan Curah Hujan dan Suhu di Jawa Tengah
Abstrak: Dengue hemorrhagic fever (DHF) is a viral disease which has spread throughout Indonesia over the past 25 years. Ae. aegypti is the main vector of the disease. In Indonesia, especially in Central Java, there has been an upward trend in the incidence of dengue hemorrhagic fever. Therefore some efforts to suppress an outbreak of this disease had been done by the government with focus against the vector. This research aims to determine and mapping the transmission potential (TP) of DHF in Central Java under 4 principal seasons: rainy (December- February), transition 1 (March – May), dry (June-August) and transition 2 (September – November) at 3 rainfall conditions ( below normal, normal and above normal years) using vectorial capacity (VC) method. The results show that the maximum average VC for under normal years occur on December-February, whereas for normal and above normal years its occurs on March-May. In every rainfall condition, the minimum VC occur on June-August when rainfall is minimum. Based on resulted map from this research, in coastal area of northern and southern site of Central Java which is lowland area, TP were higher than in central area which is highland. TP on city with high population such as Tegal, Semarang, and Surakarta were higher. Nevertheless, between VC and incidence rate (IR) were significant with coefficient correlation 0.057, caused by there weren’t interventions between 35 districts in Central Java to supress the number of DHF disease in case of sanitation, vector controls (fogging, aerial spraying, making abate), immunity of society, etc.
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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