PENGARUH STRATEGI PEMBELAJARAN DISCOVERY LEARNING BERBANTUAN MINI-MAGZ TERHADAP HASIL BELAJAR KOGNITIF BIOLOGI SISWA

Penelitian ini dilakukan karena proses pembelajaran yang digunakan guru sebelumnya adalah pembelajaran teacher centre, yang mana hal tersebut membuat siswa merasa bosan, sehingga hasil belajar yang diperoleh rendah. Maka dari itu, peneliti mencoba untuk menggunakan strategi pembelajaran discovery learning berbantuan mini magz untuk melihat hasil belajar siswa. Penelitian ini bertujuan untuk mengetahui pengaruh strategi pembelajaran discovery learning berbantuan mini-magz dalam pembelajaran biologi materi sistem ekskresi pada manusia terhadap hasil belajar kognitif biologi siswa. Desain penelitian ini yaitu non equivalent control group design pretest-posttest. Populasi penelitian ini adalah kelas XI MIA di Pesantren Fajrul Iman, yang mana sampelnya menggunakan 2 kelas dengan metode pengambilan sampel jenuh. Kelas XI MIA A sebagai kelas eksperimen yang menggunakan strategi pembelajaran discovery learning disertai dengan membuat ringkasan berformat mini magz, sedangkan kelas XI MIA B sebagai kelas kontrol menggunakan model pembelajaran konvensional. Teknik pengumpulan datanya yaitu menggunakan tes tertulis, yang terdiri dari pretest yang berjumlah 20 soal dan posttest yang berjumlah 20 soal. Data dianalisis menggunakan analisis statistik inferensial. Berdasarkan hasil penelitian, untuk uji t diperoleh bahwa thitung 3,64 sedangkan ttabel 1,71, itu berarti bahwa thitung > ttabel maka H0 ditolak dan Ha diterima. Kesimpulan dari hasil penelitian ini yaitu terdapat pengaruh strategi pembelajaran discovery learning berbantuan mini magz terhadap hasil belajar kognitif biologi siswa. Saran untuk peneliti lain yaitu apabila peneliti ingin menggunakan strategi pembelajaran discovery learning sebaiknya menyiapkan waktu yang cukup untuk mensosialisasikan strategi ini, supaya hasil yang didapatkan lebih maksimal

Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

oai:ojs.pkp.sfu.ca:article/2Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained from oil fields. Therefore, for training purposes hypothetical data are developed using the transmission pipeline model, by applying various physical configuration of pipeline and applying oil properties correlations to estimate the value of oil density and viscosity. The various leak locations and leak rates are also represented in this model. The prediction of those two leak parameters will be completed until the total error is less than certain value of tolerance, or until iterations level is reached. To recognize the pattern, forward procedure is conducted. The application of this approach produces conclusion that for certain pipeline network configuration, the higher number of iterations will produce accurate result. The number of iterations depend on the leakage rate, the smaller leakage rate, the higher number of iterations are required. The accuracy of this approach is clearly determined by the quality of training data. Therefore, in the preparation of training data the results of pressure drop calculations should be validated by the real measurement of pressure drop along the pipeline. For the accuracy purposes, there are possibility to change the pressure drop and fluid properties correlations, to get the better results. The results of this research are expected to give real contribution for giving an early detection of oil-spill in oil fields

Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained from oil fields. Therefore, for training purposes hypothetical data are developed using the transmission pipeline model, by applying various physical configuration of pipeline and applying oil properties correlations to estimate the value of oil density and viscosity. The various leak locations and leak rates are also represented in this model. The prediction of those two leak parameters will be completed until the total error is less than certain value of tolerance, or until iterations level is reached. To recognize the pattern, forward procedure is conducted. The application of this approach produces conclusion that for certain pipeline network configuration, the higher number of iterations will produce accurate result. The number of iterations depend on the leakage rate, the smaller leakage rate, the higher number of iterations are required. The accuracy of this approach is clearly determined by the quality of training data. Therefore, in the preparation of training data the results of pressure drop calculations should be validated by the real measurement of pressure drop along the pipeline. For the accuracy purposes, there are possibility to change the pressure drop and fluid properties correlations, to get the better results. The results of this research are expected to give real contribution for giving an early detection of oil-spill in oil fields

Global circulation patterns of seasonal influenza viruses vary with antigenic drift.

Understanding the spatiotemporal patterns of emergence and circulation of new human seasonal influenza virus variants is a key scientific and public health challenge. The global circulation patterns of influenza A/H3N2 viruses are well characterized, but the patterns of A/H1N1 and B viruses have remained largely unexplored. Here we show that the global circulation patterns of A/H1N1 (up to 2009), B/Victoria, and B/Yamagata viruses differ substantially from those of A/H3N2 viruses, on the basis of analyses of 9,604 haemagglutinin sequences of human seasonal influenza viruses from 2000 to 2012. Whereas genetic variants of A/H3N2 viruses did not persist locally between epidemics and were reseeded from East and Southeast Asia, genetic variants of A/H1N1 and B viruses persisted across several seasons and exhibited complex global dynamics with East and Southeast Asia playing a limited role in disseminating new variants. The less frequent global movement of influenza A/H1N1 and B viruses coincided with slower rates of antigenic evolution, lower ages of infection, and smaller, less frequent epidemics compared to A/H3N2 viruses. Detailed epidemic models support differences in age of infection, combined with the less frequent travel of children, as probable drivers of the differences in the patterns of global circulation, suggesting a complex interaction between virus evolution, epidemiology, and human behaviour.T.B. was supported by a Newton International Fellowship from the Royal Society and through NIH U54 GM111274. S.R. was supported by MRC (UK, Project MR/J008761/1), Wellcome Trust (UK, Project 093488/Z/10/Z), Fogarty International Centre (USA, R01 TW008246‐01), DHS (USA, RAPIDD program), NIGMS (USA, MIDAS U01 GM110721‐01) and NIHR (UK, Health Protection Research Unit funding). The Melbourne WHO Collaborating Centre for Reference and Research on Influenza was supported by the Australian Government Department of Health and thanks N. Komadina and Y.‐M. Deng. The Atlanta WHO Collaborating Center for Surveillance, Epidemiology and Control of Influenza was supported by the U.S. Department of 13 Health and Human Services. NIV thanks A.C. Mishra, M. Chawla‐Sarkar, A.M. Abraham, D. Biswas, S. Shrikhande, AnuKumar B, and A. Jain. Influenza surveillance in India was expanded, in part, through US Cooperative Agreements (5U50C1024407 and U51IP000333) and by the Indian Council of Medical Research. M.A.S. was supported through NSF DMS 1264153 and NIH R01 AI 107034. Work of the WHO Collaborating Centre for Reference and Research on Influenza at the MRC National Institute for Medical Research was supported by U117512723. P.L., A.R. & M.A.S were supported by EU Seventh Framework Programme [FP7/2007‐2013] under Grant Agreement no. 278433-­‐PREDEMICS and ERC Grant agreement no. 260864. C.A.R. was supported by a University Research Fellowship from the Royal Society.This is the author accepted manuscript. It is currently under infinite embargo pending publication of the final version