Academic literature on the topic 'Python 3.6'
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Journal articles on the topic "Python 3.6"
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Young, C., N. Ravida, M. Rochford, and B. Durrant. "92 Sperm Cryopreservation in the Burmese Python (Python bivittatus) as a Model for Endangered Snakes." Reproduction, Fertility and Development 30, no. 1 (2018): 185. http://dx.doi.org/10.1071/rdv30n1ab92.
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The Burmese python (Python bivittatus) is listed as vulnerable by the International Union for Conservation of Nature (IUCN). Released pet Burmese pythons have detrimental effects on fauna native to southern Florida and are responsible for localised declines of several species in some parts of the Everglades National Park (IUCN, 2012; 10.2305/IUCN.UK.2012-1.RLTS.T193451A2237271.en). As part of an invasive species monitoring program, Burmese pythons were captured in the Florida Everglades and used as a model for the development of sperm cryopreservation protocols for endangered snakes. Sperm was collected by flushing the vas deferens postmortem and initial motility score (IMS; % motile × speed of progression2), plasma membrane integrity (IPL), and acrosome integrity (IAC) were recorded before cryopreservation. Sperm was extended in TEST-yolk buffer with final dimethyl sulfoxide (DMSO) or glycerol (GLY) concentrations of 8, 12, or 16%, or combinations of DMSO and GLY with final concentrations of 4:4, 6:6, or 8:8%. Sperm in 500 µL of extender was frozen in vials at 0.3°C/min to –40°C before storage in liquid nitrogen. For each treatment, triplicate vials from each of 3 males were thawed at 37°C for 90 s. Cryoprotectant was removed by centrifugation and the sperm pellet was resuspended in TCM-199+HEPES. Sperm was evaluated at 22°C immediately following resuspension (T0) and at 60 (T60) minutes. All data were expressed as a percentage of initial (%IMS, %IPL, and %IAC). The effects of freeze method on %IMS, %IPL and %IAC were analysed by ANOVA and Tukey’s HSD test. Freeze method significantly affected %IMS at T0 (P = 0.0004) and T60 (P = 0.0001), with sperm frozen in the 6%DMSO:6%GLY and 4%DMSO:4%GLY treatments resulting in the highest %IMS at both T0 (19.4% and 17.7%, respectively) and T60 (26.7% and 14.4%, respectively). Regardless of cryoprotectant concentrations, sperm frozen in a combination of DMSO and GLY exhibited significantly higher %IMS than all treatments of DMSO or GLY alone (P < 0.0001 at T0 and T60). The %IPL was significantly affected by freeze method at T0 (P < 0.0001) and T60 (P = 0.0266). Sperm frozen in 8%DMSO:8%GLY and 6%DMSO:6%GLY retained greater %IPL at both T0 (69.1% and 65.7%, respectively) and T60 (47.8% and 49.9%, respectively). Acrosome integrity was significantly affected by freeze method at T0 (P < 0.0001) and sperm frozen in 8% DMSO resulted in the greatest %IAC (56.4%). In addition, all DMSO and DMSO:GLY treatments preserved a significantly greater proportion of intact acrosomes than GLY alone (P < 0.0001). To simplify these analyses and to determine the best overall freeze method for this species, a sperm quality index (SQI) was calculated, giving equal weight to each of the 3 measured indicators of cryosurvival. The SQI analysis revealed that Burmese python sperm frozen at 0.3°C/min in either 6%DMSO:6%GLY or 4%DMSO:4%GLY exhibited significantly higher post-thaw viability at T0 and T60 than all other treatments. This study represents the first comparative, comprehensive attempt to develop a sperm cryopreservation protocol for any snake species.
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Helmstetter, Cécile, Robert K. Pope, Mathieu T’Flachebba, Stephen M. Secor, and Jean-Hervé Lignot. "The effects of feeding on cell morphology and proliferation of the gastrointestinal tract of juvenile Burmese pythons (Python molurus)." Canadian Journal of Zoology 87, no. 12 (December 2009): 1255–67. http://dx.doi.org/10.1139/z09-110.
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The gastrointestinal tract of Burmese pythons ( Python molurus (L., 1758)) exhibits large morphological and physiological changes in response to feeding and extended periods of fasting. In this study the mucosa of the stomach, small intestine, and colon were examined for changes in structure and cellular proliferation. The mucosa of fasting pythons exhibited low levels of cellular replication, but after feeding, cellular replication was evident as early as 12 h in the small intestine and colon and 24 h in the stomach. Replication peaked 3 days postfeeding for the small intestine and colon, but was still increasing at 6 days postfeeding in the stomach. Interestingly, cell proliferation was still evident after 45 days in the colon. In these tissues, a stock of “ready-to-use” primary lysosomes is found in the mucosal cells of fasting animals, whereas profound intracellular recycling is typical of animals that have been fed. These findings indicate that during the postprandial period, the intestinal mucosa undergoes extensive remodelling in anticipation of the next fasting and feeding period. One key adaptive factor for the python’s ability to cope with infrequent feeding is a well-prepared digestive system in fasting animals that can quickly start functioning again when food becomes available.
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Oikawa, K., H. Sato, K. Watanabe, Y. H. Su, T. Shinohara, T. Kai, Y. Kiyanagi, and H. Hasemi. "Update of Bragg edge analysis software “GUI-RITS”." Journal of Physics: Conference Series 2605, no. 1 (September 1, 2023): 012013. http://dx.doi.org/10.1088/1742-6596/2605/1/012013.
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Abstract The energy-resolved neutron imaging system, RADEN at J-PARC, has been providing to users a Rietveld-type analysis code, RITS, for pulsed neutron Bragg-edge transmission (BET) imaging with a graphical user interface (GUI) version, for fitting spectral data obtained with this instrument. In the last year, we updated the computational platform of GUI-RITS software from Scientific Linux 6 (SL6) and Python 2 to Windows 10 and Python 3, and added some functions to improve usability. The license agreement for this updated software is the Berkeley Software Distribution (BSD) 2-Clause License (non-copyleft) and is currently available for download from the RADEN website.
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Sembiring, Feby Wulandari. "MENGUKUR TINGKAT AKURASI 6 MODEL REGRESI DALAM MACHINE LEARNING UNTUK ESTIMASI PENYAKIT DIABETES." JURNAL TEKNISI 4, no. 1 (February 28, 2024): 23. http://dx.doi.org/10.54314/teknisi.v4i1.1805.
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Abstract: This research aims to develop a companion application that uses the regression method as a warning system for the public in maintaining blood sugar levels to minimize the risk of diabetes based on machine learning. Diabetes is one of the diseases with the highest death rate in Indonesia, but the public's low awareness of checking blood sugar levels regularly is due to the high cost of controlling it at hospitals or clinics. This has an impact on increasing the prevalence of diabetes and state losses. This research utilizes the linear regression method with 6 regression models and 3 testing methods to find the best and most accurate model in predicting diabetes levels. Data was processed using Jupyter Notebook tools with the Python programming language. It is hoped that the results of this research can contribute to increasing public awareness of the importance of maintaining healthy blood sugar to prevent diabetes.Keywords: machine learning; regression; diabetes; warning system. Abstrak: Penelitian ini bertujuan untuk mengembangkan sebuah aplikasi pendamping yang menggunakan metode regresi sebagai warning system bagi masyarakat dalam menjaga kadar gula darah untuk meminimalisir risiko diabetes berbasis machine learning. Diabetes merupakan salah satu penyakit dengan angka kematian tertinggi di Indonesia, namun rendahnya kesadaran masyarakat dalam memeriksa kadar gula darah secara berkala disebabkan oleh mahalnya biaya pengontrolan di rumah sakit atau klinik. Hal ini berdampak pada meningkatnya prevalensi diabetes dan kerugian negara. Penelitian ini memanfaatkan metode regresi linier dengan 6 model regresi dan 3 metode pengujian untuk mencari model yang paling baik dan akurat dalam memprediksi kadar diabetes. Data diproses menggunakan tools Jupyter Notebook dengan bahasa pemrograman Python. Hasil penelitian ini diharapkan dapat memberikan kontribusi dalam meningkatkan kesadaran masyarakat akan pentingnya menjaga kesehatan gula darah untuk mencegah diabetes.Kata Kunci: machine learning; regresi; diabetes; warning system.
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Nikandrov, A. A. "Multifunctional and flexible online platforms for creating educational materials." Informatics and education 37, no. 6 (January 21, 2023): 22–29. http://dx.doi.org/10.32517/0234-0453-2022-37-6-22-29.
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The article actualizes the need to use multifunctional flexible online platforms to promote educational activities, in particular the discipline “Machine Learning”. The main characteristic features of the discipline “Machine Learning” are described, the teaching of which consists in a task-based approach through writing program codes in a programming language, which is the Python 3 interpreter with a bundle of libraries selected: NumPy, Pandas, Matplotlib and Seaborn for data processing and visualization. The Scikit-learn library is used directly for machine learning. In addition to the Python 3 interpreter, coding tools are involved, namely: the PyCharm Community cross-platform development environment and the Jupyter Notebook open source web application. The potential of educational multifunctional flexible online platforms including designers of open online courses to facilitate independent learning of students is evaluated. According to the versions of various domestic and foreign scientific publications, the most mentioned online platforms are identified, their functionality regarding the placement of material in the fields of programming and machine learning was analyzed. Based on the analysis of the functional, a group of potential basic requirements for educational platforms in teaching programming within the discipline “Machine Learning” was identified, analyzed and discussed.
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Abidin, Zaenal. "PELATIHAN DASAR-DASAR ALGORITMA DAN PEMOGRAMAN UNTUK MEMBANGKITKAN MINAT SISWA-SISWI SMK PADA DUNIA PEMOGRAMAN." Journal of Social Sciences and Technology for Community Service (JSSTCS) 2, no. 2 (September 15, 2021): 54. http://dx.doi.org/10.33365/jsstcs.v2i2.1326.
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Salah satu jurusan yang ada SMK PGRI 1 Limau yaitu jurusan Multimedia. Kegiatan PKM khusus di jurusan Multimedia dilakukan adalah Pelatihan Dasar-dasar Algoritma dan Pemograman bagi siswa SMK PGRI 1 Limau. Pelatihan ini bertujuan agar membuka cakrawala awal dan ketertarikan mereka akan dunia programming. Materi pelatihan yang telah diberikan adalah (1) Pretest, (2) Sesi 1 : Bedah Potensi Dunia Kerja bidang Komputer, (3) Sesi 2 : Algoritma dan Pemograman Dasar Runtunan di Python, (4) Sesi 3 : Algoritma dan Pemograman Dasar tentang pencabangan di Python, (5) Sesi 4 : Algoritma dan Pemograman dasar tentang perulangan di Python, dan (6) Posttest. Pelaksanaan PKM Sekolah Binaan telah dilaksanakan dengan metode ceramah di kelas dan latihan pemogragraman di laboratorium komputer milik SMK PGRI 1 Limau. Secara umum kunjungan ke SMK PGRI 1 Limau 4 kali kunjungan sesuai dengan rincian dan jadwal kegiatan yang telah dibuat. Hasi pelatihan ini terlihat dari peningkatan nilai rata-rata siswa-siswi pretest yaitu Di awal pertemuan dengan siswa-siswi SMK PGRI 1 Limau, para peserta diberikan 10 soal essay yang jawabannya bersifat open answering dan closed answering. Delapan soal open answering dan dua closed answering. Pertanyaan dibuat sesuai dengan tingkat pengetahuan siswa-siswa SMK akan soal-soal mendasar dan sederhana tentang algoritma dan pemograman. Hasil pretest menunjukannya minimnya kemampuan awal mereka yaitu rata-rata siswa-siswi mampu menjawab 1.67 pertanyaan. Kemudian hasil posttest menunjukan nilai rata-rata siswa-siswi mampu menjawab 8.33 pertanyaan. Perningkatan kemampuan siswa dalam menyelesaikan posttest meningkat sebesar 66.67 %.
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Matisakov, Zh, Zh Mambetov, and Zh Matisakov. "Modeling the Determination of Wavelength Using a Diffraction Grating in Python." Bulletin of Science and Practice 10, no. 7 (July 15, 2024): 21–25. http://dx.doi.org/10.33619/2414-2948/104/02.
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To model a laboratory experiment for determining the wavelength of light using a diffraction grating in Python, it is proposed to use the libraries numpy, matplotlib, and ipywidgets. A simulation is created to model the positions of maxima on the screen. The developed code can be run in an environment that supports ipywidgets, such as Jupyter Notebook, to obtain an interactive simulation for determining the wavelength of light using a diffraction grating. The code includes the following steps: 1. Import the necessary libraries: numpy for numerical calculations, matplotlib for plotting graphs, ipywidgets for creating interactive widgets. 2. Define constants: d - the distance between the slits of the diffraction grating, L - the distance from the grating to the screen. 3. Create the function calculate_maxima to calculate the positions of the maxima. 4. Create the function plot_diffraction_pattern to plot the diffraction pattern. 5. Create interactive widgets: wavelength_slider to change the wavelength of light, order_max_slider to change the maximum order of the interference maxima. 6. Run the interactive simulation, which allows parameters to be adjusted and results to be observed in real-time.
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Pachpute, Rushikesh, Abhishek Jadhav, Aditya Shinde, Kaustubh Naik, and S. V. Gawai. "A Survey on “Medicines at Your Fingertips”." Data Analytics and Artificial Intelligence 3, no. 2 (February 1, 2023): 144–49. http://dx.doi.org/10.46632/daai/3/2/26.
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Medicines at Your Fingertips is a website aimed to provide all the information about any medicine, it’s side effects and all the other health related information. All the data of the medicines is stored in our database and it is fetched during the execution of user’s request. We have created the frontend using HTML, CSS, JS, JQUERY, JQUERY UI and Bootstrap. The Backend is built using Django Framework of Python. Our website contains 6 main components which are: Chatbot: An intelligent chatbot which will give any information that the user has asked. The AI chatbot is created using the Pytorch library of python. It will also help the user to navigate from the whole website. Drugs a to Z, a drug dictionary to give all the information about the desired medicine. There are 2 ways to search the medicine. Pill Finder: To search the medicine alphabetically or using the search bar Phonetic Search: To search the medicine using voice command. Drugs By Condition: It contains the information about all types of health conditions, their causes and treatment along with some medicines which are used to treat them. Side Effects: It also has alphabetically sorted medicines which gives the information about the side effects of the particular medicine along with a search bar. First Aid: This part consists of 3 components which gives the information about first aid treatments and My Med List to set reminders for the doses of your medicine.
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I Komang Setia Buana. "Implementasi Aplikasi Speech to Text untuk Memudahkan Wartawan Mencatat Wawancara dengan Python." Jurnal Sistem dan Informatika (JSI) 14, no. 2 (August 4, 2020): 135–42. http://dx.doi.org/10.30864/jsi.v14i2.293.
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Wawancara merupakan kegiatan komunikasi melalui proses pertukaran informasi antara reporter dan sumber berita. Banyak wartawan lebih sibuk mencatat pada saat wawancara sehingga hasil wawancara tidak efektif. Oleh karena itu dibutuhkan alat perekam untuk merekam jawaban dari narasumber. Akan tetapi alat perekam dibutuhkan waktu untuk mendengarkan hasil rekaman, kemudian mencatatnya. Padahal dituntut untuk mengumpulkan 3 sampai 4 berita dalam sehari. Dalam ilmu komputer terdapat bidang ilmu yaitu Speech to Text, teori ini akan bermanfaat untuk kondisi tersebut. Speech to text merupakan fitur untuk mengubah suara menjadi teks. Keunggulannya adalah layanan pengenalan suara. Speech to Text berfungsi untuk memanajemen waktu agar lebih efektif. Umumnya kecepatan berbicara dengan kecepatan mencatat berbeda. Sehingga hal tersebut menyulitkan wartawan yang bertugas. Disisi lain, teori ini akan sangat berguna bagi kaum disabilitas. Karena user atau pengguna hanya perlu menggunakan suara untuk melakukan aktivitas mengetik selayaknya orang normal pada umumnya. Dari permasalahan tersebut, dibuatkan aplikasi yang bisa mengubah suara ke dalam suatu teks/tulisan dengan menggunakan bahasa pemrograman Python. Untuk melakukan proses mengubah suara menjadi teks menggunakan modul speech recognition. Uji coba menggunakan 6 sample audio hasil rekaman, didapat bahwa dengan menggunakan bahasa pemrograman Python, mampu mengonversi suara ke tulisan dengan tingkat keberhasilan mencapai 94,75 %.
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Weise, Thomas. "Software - motipy: the Metaheuristic Optimization in Python Library." ACM SIGEVOlution 16, no. 4 (December 2023): 1–2. http://dx.doi.org/10.1145/3638461.3638464.
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We are thankful for the opportunity to announce our Python package "moptipy," which offers a rich set of tools for implementing, applying, and experimenting with metaheuristic optimization algorithms. Our package has been designed with benchmarking in mind and thus provides very comprehensive experimentation, data collection, and evaluation facilities out of the box. moptipy has the following features: 1. Very comprehensive documentation with many examples, reaching down to literature references inside the code and up to complex example experiments. 2. Several standard search spaces (bit strings, permutations, real vectors), operators, and algorithms, including randomized local search/hill climbers, simulated annealing, evolutionary algorithms, memetic algorithms, NSGA-II, several numerical optimization algorithms, etc., are already implemented and ready for use. 3. It is very easy to implement new algorithms using moptipy, be they general black-box methods or tailored for specific optimization problems. 4. It is also easy to integrate algorithms from external libraries and unify them under our API, which we did as proof-of-concept with CMA-ES, BOBYQA, as well as for the algorithms from SciPy. 5. Data can be collected at different verbosity levels, ranging from only providing the final result and its quality via the API (without creating any files) to creating log files with all (or all improving) steps of an algorithm, the result, the algorithm and problem parameters, the system setup, non-dominated solutions, and the random seed for fully-reproducible experiments. [1] 6. An experiment execution facility for very simple and robust parallel and distributed experimentation is provided. Parallelization and distribution works out-of-the-box based on shared folders and thus does not require additional libraries or programming effort [1]. 7. Stopping criteria for optimization processes can be defined based on goal solution qualities, clock time, and/or consumed objective function evaluations. 8. All experiments are fully reproducible, i.e., from a log file an algorithm and problem can be configured such that, in the replication experiment, exactly the same search steps are performed as in the original setting [1]. 9. The experiment evaluation facility can parse the log files and generate progress plots, result tables, ERT and ECDF plots, statistical test tables, and export data towards Excel or the popular IOHanalyzer. 10. Both single-objective and multi-objective optimization are supported under the same unified API. 11. The package has a good unit test coverage and pre-defined tools to unit test your own code. When implementing new objective functions, algorithms, encodings, oder operators, it is possible to use these tools to look for errors.
Books on the topic "Python 3.6"
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Schäfer, Christoph. Quickstart Python. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-33552-6.
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Gianfagna, Leonida, and Antonio Di Cecco. Explainable AI with Python. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68640-6.
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Hart, William E., Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Gabriel A. Hackebeil, Bethany L. Nicholson, and John D. Siirola. Pyomo — Optimization Modeling in Python. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58821-6.
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Gray, Iain. Snake Charming - The Musical Python. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60660-6.
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Langtangen, Hans Petter, ed. Python Scripting for Computational Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-73916-6.
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Haslwanter, Thomas. Hands-on Signal Analysis with Python. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57903-6.
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Haslwanter, Thomas. An Introduction to Statistics with Python. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28316-6.
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Zuckarelli, Joachim L. Learn coding with Python and JavaScript. Wiesbaden: Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-42912-6.
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Lee, Kent D., and Steve Hubbard. Data Structures and Algorithms with Python. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-42209-6.
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Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30717-6.
Full textBook chapters on the topic "Python 3.6"
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Matuszek, David. "Graphical User Interfaces." In Quick Python 3, 83–89. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003356219-6.
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Köhl, Maximilian A., Michaela Klauck, and Holger Hermanns. "Momba: JANI Meets Python." In Tools and Algorithms for the Construction and Analysis of Systems, 389–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72013-1_23.
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AbstractJANI-model [6] is a model interchange format for networks of interacting automata. It is well-entrenched in the quantitative model checking community and allows modeling a variety of systems involving concurrency, probabilistic and real-time aspects, as well as continuous dynamics. Python is a general purpose programming language preferred by many for its ease of use and vast ecosystem. In this paper, we present Momba, a flexible Python framework for dealing with formal models centered around the JANI-model format and formalism. Momba strives to deliver an integrated and intuitive experience for experimenting with formal models making them accessible to a broader audience. To this end, it provides a pythonic interface for model construction, validation, and analysis. Here, we demonstrate these capabilities.
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Baihaqi, Muhammad Yeza, Vincent, and Joni Welman Simatupang. "Real-Time Hand Gesture Recognition for Humanoid Robot Control Using Python CVZone." In Innovations in Smart Cities Applications Volume 6, 262–71. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26852-6_24.
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"6. Schleifen." In Python 3, 69–72. De Gruyter Oldenbourg, 2016. http://dx.doi.org/10.1515/9783110473650-007.
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"Chapter 6: Classes." In Python 3, 111–30. De Gruyter, 2017. http://dx.doi.org/10.1515/9781683923053-007.
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"6. Muster In Zeichenketten." In Python 3, 103–12. De Gruyter Oldenbourg, 2018. http://dx.doi.org/10.1515/9783110544138-007.
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"Chapter 6: Feature Engineering." In Python 3 and Feature Engineering, 141–54. De Gruyter, 2023. http://dx.doi.org/10.1515/9781683929482-007.
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"Chapter 6: ChatGPT and Python Code." In Python 3 using ChatGPT/GPT4, 157–78. De Gruyter, 2023. http://dx.doi.org/10.1515/9781501518737-007.
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"Chapter 6: Seaborn for Data Visualization." In Python 3 and Data Visualization, 195–222. De Gruyter, 2023. http://dx.doi.org/10.1515/9781683929451-007.
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"Chapter 6 Classifiers in Machine Learning." In Python 3 for Machine Learning, 179–208. De Gruyter, 2020. http://dx.doi.org/10.1515/9781683924937-007.
Full textConference papers on the topic "Python 3.6"
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Fong, Jeffrey T., Roland deWit, Pedro V. Marcal, James J. Filliben, N. Alan Heckert, and Stephen R. Gosselin. "Design of a PYTHON-Based Plug-In for Benchmarking Probabilistic Fracture Mechanics Computer Codes With Failure Event Data." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77974.
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In a 2007 paper entitled “Application of Failure Event Data to Benchmark Probabilistic Fracture Mechanics (PFM) Computer Codes” (Simonen, F. A., Gosselin, S. R., Lydell, B. O. Y., Rudland, D. L., & Wikowski, G. M. Proc. ASME PVP Conf., San Antonio, TX, Paper PVP2007-26373), it was reported that the two benchmarked PFM models, PRO-LOCA and PRAISE, predicted significantly higher failure probabilities of cracking than those derived from field data in three PWR and one BWR cases by a factor ranging from 30 to 10,000. To explain the reasons for having such a large discrepancy, the authors listed ten sources of uncertainties: (1) Welding Residual Stresses. (2) Crack Initiation Predictions. (3) Crack Growth Rates. (4) Circumferential Stress Variation. (5) Operating temperatures different from design temperatures. (6) Temperature factor in actual activation energy vs. assumed. (7) Under reporting of field data due to NDE limitations. (8) Uncertainty in modeling initiation, growth, and linking of multiple cracks around the circumference of a weld. (9) Correlation of crack initiation times and growth rates. (10) Insights from NUREG/CR-6674 (2000) fatigue crack growth models using conservative inputs for cyclic strain rates and environmental parameters such as oxygen content. In this paper we design a Python-based plug-in that allows a user to address those ten sources of uncertainties. This approach is based on the statistical theory of design of experiments with a 2-level factorial design, where a small number of runs is enough to estimate the uncertainties in the predictions of PFM models due to some combination of the source uncertainties listed by Simonen et al (PVP2007-26373).
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Modi, Gaurav, Manu Ujjwal, and Srungeer Simha. "Short Term Injection Re-Distribution STIR: Real-Time Waterflood Optimization Technique Using Advanced Data Analytics." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205593-ms.
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Abstract Short Term Injection Re-distribution (STIR) is a python based real-time WaterFlood optimization technique for brownfield assets that uses advanced data analytics. The objective of this technique is to generate recommendations for injection water re-distribution to maximize oil production at the facility level. Even though this is a data driven technique, it is tightly bounded by Petroleum Engineering principles such as material balance etc. The workflow integrates and analyse short term data (last 3-6 months) at reservoir, wells and facility level. STIR workflow is divided into three modules: Injector-producer connectivity Injector efficiency Injection water optimization First module uses four major data types to estimate the connectivity between each injector-producer pair in the reservoir: Producers data (pressure, WC, GOR, salinity) Faults presence Subsurface distance Perforation similarity – layers and kh Second module uses connectivity and watercut data to establish the injector efficiency. Higher efficiency injectors contribute most to production while poor efficiency injectors contribute to water recycling. Third module has a mathematical optimizer to maximize the oil production by re-distributing the injection water amongst injectors while honoring the constraints at each node (well, facility etc.) of the production system. The STIR workflow has been applied to 6 reservoirs across different assets and an annual increase of 3-7% in oil production is predicted. Each recommendation is verified using an independent source of data and hence, the generated recommendations align very well with the reservoir understanding. The benefits of this technique can be seen in 3-6 months of implementation in terms of increased oil production and better support (pressure increase) to low watercut producers. The inherent flexibility in the workflow allows for easy replication in any Waterflooded Reservoir and works best when the injector well count in the reservoir is relatively high. Geological features are well represented in the workflow which is one of the unique functionalities of this technique. This method also generates producers bean-up and injector stimulation candidates opportunities. This low cost (no CAPEX) technique offers the advantages of conventional petroleum engineering techniques and Data driven approach. This technique provides a great alternative for WaterFlood management in brownfield where performing a reliable conventional analysis is challenging or at times impossible. STIR can be implemented in a reservoir from scratch in 3-6 weeks timeframe.
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Karalus, Megan F., K. Boyd Fackler, Igor V. Novosselov, John C. Kramlich, and Philip C. Malte. "A Skeletal Mechanism for the Reactive Flow Simulation of Methane Combustion." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-95904.
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A skeletal mechanism for the prediction of NOx emissions from methane combustion at gas turbine conditions is developed in the present work. The goal is a mechanism that can be used in computational fluid dynamic modeling of lean premixed (LPM) combustors. A database of solutions from 0-D, adiabatic, homogeneous reactors (PSRs) is computed using CHEMKINPRO [1] over a parameter space chosen to include pressures from 1 to 30 atm, equivalence ratios from 0.4 to 1.0, and mean PSR residence times from slightly greater than blowout to 3ms. A resisidence time of 3 ms represents a useful maximum for the super-equilibrium flame zone where most of the NOx forms in LPM combustors. Fuel oxidation and NOx formation are treated separately in the reduction process. The method of Directed Relation Graph (DRG) is applied for methane oxidation and its extension, DRG-aided sensitivity analysis (DRGASA), is used to determine the skeletal NOx mechanism to append to the methane mechanism. Post-processing of the PSR solution database and implementation of the reduction algorithm are accomplished in SAGE [2], a Python based, open-source mathematics software package. The skeletal oxidation and NOx mechanisms are validated against full GRI 3.0 [3] in both PSR and laminar flame speed calculations. When compared with the detailed GRI 3.0 mechanism, NOx emissions are predicted within 7% near blowout and 3% at 3ms, and laminar flame speeds are predicted within 20% over the range of equivalence ratios and pressures. The skeletal mechanism is presented here and it should be noted that all reactions of the H2/CO submechanism are retained. The skeletal mechanism consists of 22 species and 122 reactions for methane oxidation and an additional 8 species and 55 reactions to describe NOx formation (30 species, 177 reactions total). The final skeletal mechanism with NOx chemistry is available for download here [4]. To demonstrate the predictive capability of the validated mechanism in a reactive flow system, it is implemented in an ANSYS Fluent model of a single jet stirred reactor, the results of which are compared to experimental reactor data presented in [5] and [6]. Predicted and measured profiles of temperature and NOx emissions are shown. Temperature and NOx emissions compare well in the recirculation zone of the JSR, although both NOx emissions and temperature are under-predicted in the jet region. Finally, the contribution of each chemical pathway for NOx formation is evaluated.
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Ojah, M. G., E. S. Adewole, and E. Emumena. "Pressures and Pressure Derivatives of Vertical and Horizontal Wells Located Within Intersecting Sealing Fault and Constant Pressure Boundary." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/217103-ms.
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Abstract The optimization of the performance of oil and gas wells (whether vertical or directional) as well as well location from external reservoir boundaries or faults has been a major concern of the reservoir engineer over the years. This work presents an accurate method for evaluating the performance of both the vertical and horizontal wells each located within an intersecting sealing/no-flow boundary and a constant-pressure boundary. The main aim of this study was to investigate the transient pressure behaviour of a vertical well as well as a horizontal well located within an intersecting sealing fault and a constant pressure boundary. The methods employed in computing the dimensionless pressures and dimensionless pressure derivatives for both well types include the method of images and principle of superposition. The computations were also made using Microsoft Excel, Python and MATLAB software. The results obtained show that for the selected parameters; 1) the models give accurate estimation of distances between active and image wells, PD and PD’, 2) at 30 hours of production, both wells completely overcome the effects of the boundaries at 2000 ft. equidistant to faults, 3) for the infinite-acting reservoir, a characteristic values of PD’ for the vertical and horizontal wells are 0.5 and 0.2 respectively, 4) for both well types, the effect of the upper boundary is greatly felt between distances of 5.00ft. and 10.00 ft., and beyond this region, the effect of the lower boundary becomes gradually felt and then, greatly felt beyond 15.00 ft. The relationship between the pressures and unequal faults distances has no maximum or minimum points, 5) the point with the least effect of either or both boundaries as well as longest transient period is point 3 (equal distance of 15.00 ft. from both boundaries). This is the point of optimal productivity, 6) for a given distance, both PD and PD’ decrease as horizontal well length L increases, 7) for all the cases considered and dimensionless time tD, both PD and PD’ decrease with increasing horizontal well length. The longer the well length, the lower the drawdown required to give same effects, as would shorter lengths, on the well performance at a given time of production thereby prolonging production over time, and 8) for a given distance, the horizontal well length has no impact on the flow periods. The type curves can be used for matching of actual pressure drawdown data and determining the drainage area and relative well location with respect to physical boundaries. Worthy of future research are similar works on; 1) anisotropic reservoir, 2) larger values of faults distances, and 3) angles other than basic angles.
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Tieghi, Lorenzo, Alessandro Corsini, Giovanni Delibra, and Gino Angelini. "Assessment of a Machine-Learnt Adaptive Wall-Function in a Compressor Cascade With Sinusoidal Leading Edge." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91238.
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Abstract Near-wall modelling is one of the most challenging aspects of CFD computations. In fact, integration-to-the-wall with low-Reynolds approach strongly affects accuracy of results, but strongly increases the computational resources required by the simulation. A compromise between accuracy and speed to solution is usually obtained through the use of wall functions, especially in RANS computations, which normally require that the first cell of the grid to fall inside the log-layer (50 < y+ < 200) [1]. This approach can be generally considered as robust, however the derivation of wall functions from attached flow boundary layers can mislead to non-physical results in presence of specific flow topologies, e.g. recirculation, or whenever a detailed boundary layer representation is required (e.g. aeroacoustics studies) [2]. In this work, a preliminary attempt to create an alternative data-driven wall function is performed, exploiting artificial neural networks (ANNs). Whenever enough training examples are provided, ANNs have proven to be extremely powerful in solving complex non-linear problems [3]. The learner that is derived from the multi-layer perceptron ANN, is here used to obtain two-dimensional, turbulent production and dissipation values near the walls. Training examples of the dataset have been initially collected either from LES simulations of significant 2D test cases or have been found in open databases. Assessments on the morphology and the ANN training can be found in the paper. The ANN has been implemented in a Python environment, using scikit-learn and tensorflow libraries [4][5]. The derived wall function is implemented in OpenFOAM v-17.12 [6], embedding the forwarding algorithm in run-time computations exploiting Python3.6m C_Api library. The data-driven wall function is here applied to k-epsilon simulations of a 2D periodic hill with different computational grids and to a modified compressor cascade NACA aerofoil with sinusoidal leading edge. A comparison between ANN enhanced simulations, available data and standard modelization is here performed and reported.
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Albeanu, Grigore. "USING MOODLE FOR TEACHING SCIENCE AND INFORMATION TECHNOLOGY. TOWARDS HIGHER EDUCATION REENGINEERING." In eLSE 2015. Carol I National Defence University Publishing House, 2015. http://dx.doi.org/10.12753/2066-026x-15-088.
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This paper describes results obtained by experiments on using Moodle for teaching Science and Information Technology. Modern graphical and dedicated languages are used to create content and embed the artifacts in four Moodle courses: Programming Paradigms (PP), Internet Programming Techniques (IPT), Graphical User Interface Design (GuiD), and Multimedia Techniques (MT). The following aspects are detailed and discussed: course format, HTML5 playing, SVG, CSS3, and JavaScript coding. The GuiD & MT courses, in old versions, will be reengineered to meet the new requirements imposed by web 2.0 e-learning technologies. The following challenging topics highgliht the efficiency of Moodle: PP(Programming in Python, Programming in Common Lisp, Programming in Prolog), IPT (Client-Server applications, Developing HTML5 complient content, Quality improvement by CCS3, Dynamic behaviour improvement by JavaScript and PHP, Server side applications, distributed data base connections), Guid (Graphical Interfaces in C# and Java, Android applications), and MT (Image standards, Audio-Video standards, Multimedia databases, Multimedia Security). Due to such a large plethora of objects, the Moodle platform is full investigated related on the support offered to build high quality e-Learning content. Interoperability assurance techniques are considered to import/export content. Not only standards on Multimedia artifacts, but the conformity with e-Learning standards are considered. Many types of resources are used to illustrate both the content of course's units, and the Moodle support. Statistical results on usage Moodle for Teaching (blended learning classes) two courses (PP & ITP) are presented in order to compare different type of content used for the same class. Preliminary references: 1. Ursache L., Vaju G., Donici G., and Herman C., Moodle: Administrare, Utilizare, Evaluare. Arad, 2011. 2. Retalis S., Dougiamas M. (eds), 1st Moodle Research Conference, Heraklion, Crete-Greece, September, 14-15, 2012. 3. de Raadt M., Ratalis S., 2nd Moodle Research Conference, Sousse, Tunisia, October, 4-6, 2013. 4. ***. Teaching with Moodle, https://moodle.org/ 5. Syllabus: ITP, PP, GuiD, MT (Departement of Information Technology)
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Stupans, Andrejs, Pavels Maksimkins, Armands Senfelds, and Leonids Ribickis. "Development and testing of automated control system for sea buckthorn berry harvesting robot ""Agrobot""." In 23rd International Scientific Conference Engineering for Rural Development. Latvia University of Life Sciences and Technologies, Faculty of Engineering and Information Technologies, 2024. http://dx.doi.org/10.22616/erdev.2024.23.tf175.
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The paper describes the control system of an autonomous agriculture robot and evaluates its operation using a laboratory stand and a digital environment. AgroBot is an autonomous sea buckthorn berry harvesting robot. It automatically finds the bush branches to harvest, cuts them, and stores them in a box. AgroBot consists of a 3 DOF (Degrees of Freedom) Cartesian mobile platform and a Hyundai HH7 industrial robot arm with 6 DOF. The control system is specifically designed for real-time operation, enabling AgroBot to adapt to dynamic environmental conditions (wind, varying light) that complicate target tracking. The control system consists of two modules operating as two separate programs. The first module is the Computer Vision Module (CVM) which has high-level control of AgroBot operation. It uses feed from cameras to find the cutting spot and sends commands to approach, cut, store, and search. The module is written in Python. The second module is the Robot Control Module (RCM) which receives high-level commands from CVM and manages lower-level control of the Hyundai Controller (HC). RCM calculates the robot trajectory to a target position, communicates with both CVM and HC in parallel threads and handles errors. As HC requires time-critical control, RCM is written in C + + . RCM and CVM are running on one computer communicating via sockets. As the mobile platform hardware is in the development stage and is not available at the moment, the digital twin of the robot is created to test the system’s performance in a simulated environment. The input to the digital twin is the same as for the actual robot. It is the x, y, z position and orientation A, B, C using Euler angles. The digital twin visualization is developed in the Unity game engine. Matlab Robotic Toolbox is used with the Levenberg-Marquardt solver algorithm to calculate inverse kinematics of 9 DOF robot. The paper focuses on the Robot Control Module architecture and control system’s testing.
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Younk, Nathaniel, and B. Todd Hoffman. "Employing Regression Analysis to Demonstrate the Impact of Hyperbolic Decline Curve Parameters on Long-Term Production Forecast Accuracy for Unconventional Oil and Gas Production in the Bakken and Barnett." In SPE Western Regional Meeting. SPE, 2022. http://dx.doi.org/10.2118/209332-ms.
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Abstract Forecasting production from unconventional reservoirs is challenging because of the uncertainty that arises from intricate fracture networks, complex transport mechanisms, and convoluted flow configurations. The accuracy of decline curve analysis for such reservoirs has been questioned due to the limited amount of long-term production data available. That being so, some unconventional reservoirs, such as the Bakken and the Barnett, have produced for 15-20 years, providing an adequate amount of data to validate the accuracy of the hyperbolic decline curve method, shed light on proper parameters – b and Di, and determine the amount of production history necessary to trust regression techniques. To test this, an extensive and versatile regression analysis model was built in Python using least squares optimization to match specific durations of production data – first 6 months, first year, first two years, etc. The model outputs the optimal parameters – b and Di –to match the specific duration. Additionally, fixed b values from 0.5 to 1.5 are tested where only Di is optimized through the model. To understand how accurately the models predict production, they are validated against the most recent 5 years of data, which was not included in the matching period. For a statistically significant sample size, around 700 wells in the Bakken and 1800 wells in the Barnett with start dates between 2005 and 2010 were used. The results show that in order to have confidence in the model's ability to predict production, more than 3 years of production data must be available. If 3 years of data is not available, the hyperbolic exponent, b, should be set close to 1.0 for Bakken wells (and likely other unconventional liquid rich wells) and between 1.0 and 1.2 for Barnett wells (and likely other unconventional gas wells). Additionally, the initial nominal decline rate, Di, should be chosen in accordance with the hyperbolic exponent. Not only do these guidelines result in satisfactory, long-term predictions, but they mitigate any significant error influenced by the underlying relationships between b and Di. These curve-altering relationships induce both positive and negative impacts on the predictions. If b is improperly chosen, overestimation in late-life production profiles may ensue. Alternatively, if Di is improperly chosen, early-life production may be too high. Since production forecasting is a necessity for a company to determine its present value, this paper provides knowledge and guidance regarding forecasting procedures and parameter settings for North American unconventional operators. Using decline curve analysis to accurately predict oil and gas rates is pertinent to the longevity of these unconventional reservoirs.
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Gutiérrez Bedoya, Rubén Dario, Claudio Marcelo Fonseca, and Michelle Alba Naranjo Leon. "Solid Fast-Track Evaluation Methodology: Supporting the Decision-Making Process in the Development of Mature Assets." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206378-ms.
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Abstract As most oilfields in Ecuador are approaching to the end of the service contracts under an advanced degree of maturity, it was imperative to implement a fast-track integrated methodology that supports the decisionmaking process during assets' evaluation. This practice aimed to identify new business opportunities and assure the rehabilitation of brownfields. These fields became a target for investors willing to intervene in new joint ventures with moderate risk to boost production and returns. The methodology is prepared to overcome specific challenges such as severe reservoir pressure depletion, harsh water management issues, facilities constraints and integrity. All this while keeping economics and safe operational standards. This process is divided into five stages: First, the diagnosis of field challenges and associated risks, so that review the current status of subsurface and surface aspects. Then, the following three parallel phases are focused on the study of reservoir architecture, dynamics and performance. Finally, the remaining potential of the asset is assessed by integrating action plans to take advantage of current facilities capacities. This workflow was implemented for the evaluation of three assets: Asset 1: Mature field with a secondary gas cap where its current reservoir pressure is 800 psia (initial pressure 4,200 psia). The asset was evaluated in fifteen (15) days resulting in an integrated solution with 14 activities: conversions to injectors, water source, upsizing, reactivations, change zone, and new wells. The results presented an incremental recovery factor of 6% (by 2028) with an expected production peak of 3,500 BOPD (by 2021). Asset 2: A field producing from two main reservoirs with harsh water management issues under a non-monitored waterflooding scheme with challenging sweet spots identification was evaluated in 10 days, resulting in a redevelopment plan considering: production losses optimization, sixteen (16) activities: workovers, dual completions, new wells, reentry, shut-in, and conversion to water injectors. This evaluation delivered an incremental recovery factor of 10% (by 2029). Asset 3: Producing for around one-hundred (100) years with 3,000 wells drilled. There was a lack of pressure support and facilities and well completions integrity. The fast-track assessment focused on production optimization lasted fifteen (15) days, resulting in one-hundred eighteen (118) wells for reactivation representing an additional recovery factor of 3% (by 2029). This work supported the process for contract's renegotiation and assets' acquisition. This integrated methodology aimed to maximize the assets' value while considering the involved shareholders' needs. Each asset was analysed in an integrated and collaborative manner through the propper resources identification and the usage of the latest technology and workflows. High-resolution reservoir simulation, complex python scripts, and a chemical processes simulator were used to perform an in-depth evaluation and meet the expectations.
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Mad Sahad, Salbiah, Nian Wei Tan, Muhammad Sajid, and Ernest Austin Jones. "Enhancing Channelised Features Interpretability Using Deep Learning Predictive Modelling." In SPE Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210668-ms.
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Abstract Objectives/Scope The growth of seismic attributes technology in accelerating the advancement of exploration and development started in late 1960s, with identification of bright spots through seismic reflection traits. In between the captured timeline, pattern recognition or neural network-based analysis on seismic attributes since 1990s gradually bring significant improvement in defining structural or depositional environment. Dunham (2020) addressed insufficient labelled geophysical data for supervised classification challenges lead to the possibility of overtraining for small-scale labelled datasets and unstable predictions for unlabeled datasets. Automated 3D interpretation using CNN produced less convincing accuracy on dataset with larger incised channel (Gao, 2021). We explore an approach to improve channelized features prediction accuracy through Deep Learning (DL) algorithms by refining the quality of the input dataset. Methods, Procedures, Process We developed supervised learning method using pixel-based segmentation and transformed matrix data into structural interpretation by constraining multi seismic attributes with ground truth dataset. It is divided into two (2) steps: 1a) Exploratory Data Analysis (EDA) where sixteen (16) seismic attributes representing categories defined by Roden (2015) were extracted and normalized for feature's correlation; 1b) We choose fluvial dominated field dataset located in Malay Basin and use Python-based Labelling Tool (PyLT) to annotate 64 time slices penetrating I-27 to I-35 Lower Reservoir of Mid Miocene. Data cube were 2 ms-sampled with size of 1024 × 256 (inlines, crosslines) annotated in z-direction. Associated fluvial facies elements were interpreted into five (5) classes; 2) Application of DL in data augmentation and conducted hyperparameter testing to find the network configuration for optimal performance. Results, Observations, Conclusions A range of learning rates have been tested, from 1e-4 to 5e-6 at increments of 5e-6. We have settled on a learning rate of 5e-5, which provided the fastest training time without adversely affecting the training process by introducing unwanted instabilities. Small weight decay multiplier of 1e-3 is used to introduce weight regularization to mitigate overfitting. We tested various batch sizes from 8 to 128, in increments of powers of two. From our observation, increasing batch size led to increases in training speed and improved training stability. We have applied various range of drop out to prevent overfitting by removing unnecessary features during training stage and stabilized predictions while being the regularizer. It was observed dropout slows down the learning rate but yielded performance improvements on test dataset when increased from 0 to 0.3. Figure 1 (a) portrays two (2) inline sections representing spectral decomposition volumes with Log Pattern Analysis from nearby key wells where we can observe: Figure 1 Integrated methodology applied in this study consists of four (4) main steps: 1. Exploratory Data Analysis (EDA) for input data; 2. Define the ground truth based on two (2) scenarios; 3. Training the dataset and monitor the model's performance; 4. Geobodies extraction to be integrated with other relevant dataset. Figure 1 (b – input column), seismic attributes example (inset shown the Sweetness attributes) as part of the input data. We have chosen two (2) different time slices penetrating the studied channelized reservoir interval to identify different fluvial structures. Figure 1b (ground truth column) depicted dataset built using labelling tool. (Figure 1b – Prediction's column) shows It was observed that neural network is capable to capture distributary channels missed by attributes in binary classifications and Mud-Filled Channel (MFC) still can be detected through multi-facies classification even though facing small training dataset in Class III, as observed in deeper slice. Novel/Additive Information We have adopted DL method to alleviate detection of fluvial facies despite dealing with limited dataset. Diverse augmentation method was applied to increase the accuracy of model after we have satisfied with the refined annotated ground truth dataset. Random flipping and rotations from 0° to 45° were applied to all tiles and corresponding labels to prevent possibilities of data overfitting and helps to generalize potential aspects of features that DL algorithms have not seen before.
Reports on the topic "Python 3.6"
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Шокалюк, С. В., and Г. Ю. Руденко. Розширення можливостей Web-СКМ SAGE. Міністерство регіонального розвитку та будівництва України, 2008. http://dx.doi.org/10.31812/0564/1223.
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Відкритий характер системи SAGE дає можливість додавання до неї нових функцій, типів та класів, створювати нові бібліотеки та інтегрувати у неї нові програми як: 1) сценарії SAGE; 2) сценарії на Python з використанням бібліотеки SAGE; 3) програми на C/C++, інтегрованими засобами Cython; 4) код Cython; 5) програма мовою СКМ (наприклад, сценарій Maxima); 6) будь-яка комбінація з пп. 1–5.