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MSSANZ 19th Biennial Congress on Modelling and Simulation (MODSIM)

作者： Chopard, J. Pradal, C. Barbeau, D. Cokelaer, T. Godin, C. VirtualPlants Team INRIA France Center of Excellence for Climate Change Woodland and Forest Health University of Western Australia Australia School of Plant Biology UWA Institute of Agriculture University of Western Australia Australia

ISBN: (纸本)9780987214317

For many years a large collection of models have been developed to describe plants (e.g., trees, crops). However, few of them can be reused directly to be integrated into more complex models or combined with other plant models to answer scientific questions. This leads to a loss of time spent in re-implementing published models before starting new projects. To tackle this challenge, a platform called OpenAlea has been developed over the last 10 years with the aim of sharing models and tools among the OpenAlea community. This platform uses the well-known python programming language to link heterogeneous components/models together. It performs the evaluation of complex models expressed as a dataflow where each node is a different piece of the model. A visual front-end, VisuAlea, allows users to assemble different components together to answer practical questions without writing a single line of code. In this article, we propose to demonstrate the functioning of the platform with a single model/component. As a first step, this component will be connected to statistical components to demonstrate the ability of the platform to share common analysis and plotting functions. In a second step, the same component, instead of taking fixed parameters, will be attached to external models that will provide estimated values for these parameters, showing the ability of the platform to connect models developed by different teams. The third step will be to connect a sensitivity analysis component to this system. Finally, we will propose a different evaluation of the component as part of a temporal simulation to demonstrate how a simple static model can be reused to construct complex plant growth models.

关键词： Dataflow python programming language functional structural plant modelling

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Optics Simulations: A python Workshop 14

Optics Simulations: A Python Workshop

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14th Conference on Education and Training in Optics and Photonics (ETOP)

作者： Ghalila, H. Ammar, A. Varadharajan, S. Majdi, Y. Zghal, M. Lahmar, S. Lakshminarayanan, V. Univ Tunis El Manar Lab Spect Atom Mol & Applicat Fac Sci Tunis Tunis Tunisia Soc Tunisienne Opt Tunis Tunisia Univ Waterloo Sch Optometry & Vis Sci Waterloo ON N2L 3G1 Canada LV Prasad Eye Inst Brien Holden Inst Optometry & Vis Sci Hyderabad Andhra Prades India LV Prasad Eye Inst Prof Brien Holden Eye Res Ctr Hyderabad Andhra Prades India Univ Carthage Engn Sch Commun Tunis SupCom GreSCom Lab Ghazala Technopk Ariana 2083 Tunisia

ISBN: (数字)9781510613829

ISBN: (纸本)9781510613829;9781510613812

Numerical simulations allow teachers and students to indirectly perform sophisticated experiments that cannot be realizable otherwise due to cost and other constraints. During the past few decades there has been an explosion in the development of numerical tools concurrently with open source environments such as python software. This availability of open source software offers an incredible opportunity for advancing teaching methodologies as well as in research. More specifically it is possible to correlate theoretical knowledge with experimental measurements using "virtual" experiments. We have been working on the development of numerical simulation tools using the python program package and we have concentrated on geometric and physical optics simulations. The advantage of doing hands-on numerical experiments is that it allows the student learner to be an active participant in the pedagogical/learning process rather than playing a passive role as in the traditional lecture format. Even in laboratory classes because of constraints of space, lack of equipment and often-large numbers of students, many students play a passive role since they work in groups of 3 or more students. Furthermore these new tools help students get a handle on numerical methods as well simulations and impart a "feel" for the physics under investigation.

关键词： Active learning Optics simulations python programming language Numerical experiments

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Exploring Advanced Statistical Data Analysis Techniques for Interpolating Missing Observations and Detecting Anomalies in Mining Influenced Water Data

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ACS ES&T WATER 2023年第3期4卷 1036-1045页

作者： More, Kagiso S. Wolkersdorfer, Christian Tshwane Univ Technol SARChI Chair Mine Water Management Dept Environm Water & Earth Sci ZA-0001 Pretoria South Africa

Collecting mining influenced water (MIW) quality datacan resultin incomplete data sets with missing values and anomalies, makingit challenging to use the data for optimizing mine water *** work explores advanced statistical data analysis approaches foraddressing missing data interpolation and anomaly detection in MIWdata sets. The study compares the performance of five different interpolationtechniques and four different anomaly detection techniques using supervisedand unsupervised machine learning algorithms developed using python3.8.16. The results of the study demonstrate that the radial basisfunction, spline, and k-nearest-neighbors interpolationtechniques, along with the predictive confidence interval level anomalyapproach based on gradient boosting regression trees, perform bestfor missing data interpolation and anomaly detection, *** application of these advanced techniques can improve theaccuracy and reliability of mine water quality data, which is crucialfor making conclusions on the safety of the environment, public health,and effective MIW management. This paper highlights the importanceof developing effective methods for addressing missing data and anomaliesin MIW data sets, which can ultimately lead to improved treatmentplant optimization. This studyexplores advanced statistical data analysis approachesfor missing data interpolation and anomaly detection in mining influencedwater data, demonstrating how they can improve the accuracy and reliabilityof mine water quality data analysis.

关键词： Supervised and Unsupervised Machine Learning Algorithms python programming language Mining Influenced Water Data Missing Data Interpolation Anomaly Detection

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An Overview of the Julia programming language 18

An Overview of the Julia Programming Language

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International Conference on Computing and Big Data (ICCBD)

作者： Cabutto, Tyler A. Heeney, Sean P. Ault, Shaun V. Mao, Guifen Wang, Jin Valdosta State Univ Dept Comp Sci Valdosta GA 31698 USA Valdosta State Univ Dept Math Valdosta GA USA

ISBN: (纸本)9781450365406

The paper reviews what the Julia programming language is and its staying power relative to other popular programming languages. The following focuses on two popular programming languages (C programming and python programming) to obtain a deeper understanding of what Julia derives from them, how Julia deviates from them, and what Julia leverages to be a more reasonable choice for performance.

关键词： Julia programming language C programming language python programming language FORTRAN

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USING THE Ipython NOTEBOOK AS THE COMPUTING PLATFORM FOR SIGNALS AND SYSTEMS COURSES

USING THE IPYTHON NOTEBOOK AS THE COMPUTING PLATFORM FOR SIG...

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2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

作者： Lovejoy, McKenna R. Wickert, Mark A. Univ Colorado Dept Elect & Comp Engn Colorado Springs CO 80933 USA

ISBN: (纸本)9781467391696

The use of open-source python as opposed to traditional computing platforms such MATLAB, Mathematica, and C/C++, is becoming more and more noticeable as all forms of open-source software develop. The python user community itself is very vibrant, but what really stands out for those of us in signals and systems, is what is happening in the numerical computing side of python. This paper will describe how in particular, the Ipython notebook can be used as an analysis and simulation tool for teaching signals and systems courses. Specific code modules have been developed to augment existing python code contained in the *** module. Case studies will be used to demonstrate the capabilities of the Ipython notebook to augment lecture material with live calculations and simulations. Additionally, examples of how the Ipython notebook has been successfully used by students for homework problems, computer projects and lab reports will be illustrated. Both student and industry team members in subcontract work, have responded favorably to the use of python as an engineering problem solving platform.

关键词： scientific computing python programming language algorithms numerical computing teaching

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PyMTL: A Unified Framework for Vertically Integrated Computer Architecture Research 47

PyMTL: A Unified Framework for Vertically Integrated Compute...

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47th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Lockhart, Derek Zibrat, Gary Batten, Christopher Cornell Univ Sch Elect & Comp Engn Ithaca NY 14850 USA

ISBN: (纸本)9781479969982

Technology trends prompting architects to consider greater heterogeneity and hardware specialization have exposed an increasing need for vertically integrated research methodologies that can effectively assess performance, area, and energy metrics of future architectures. However, constructing such a methodology with existing tools is a significant challenge due to the unique languages, design patterns, and tools used in functional-level (FL), cycle-level (CL), and register-transfer-level (RTL) modeling. We introduce a new framework called PyMTL that aims to close this computer architecture research methodology gap by providing a unified design environment for FL, CL, and RTL modeling. PyMTL leverages the python programming language to create a highly productive domain-specific embedded language for concurrent-structural modeling and hardware design. While the use of python as a modeling and framework implementation language provides considerable benefits in terms of productivity, it comes at the cost of significantly longer simulation times. We address this performance-productivity gap with a hybrid JIT compilation and JIT specialization approach. We introduce SimJIT, a custom JIT specialization engine that automatically generates optimized C++ for CL and RTL models. To reduce the performance impact of the remaining unspecialized code, we combine SimJIT with an off-the-shelf python interpreter with a meta-tracing JIT compiler (PyPy). SimJIT+PyPy provides speedups of up to 72x for CL models and 200x for RTL models, bringing us within 4-6x of optimized C++ code while providing significant benefits in terms of productivity and usability.

关键词： C++ language computer architecture program compilers program interpreters CL FL JIT specialization approach JIT specialization engine PyMTL PyPy python interpreter python programming language RTL modeling Sim JIT concurrent-structural modeling cycle-level modeling design patterns domain-specific embedded language functional-level modeling hybrid JIT compilation meta-tracing JIT compiler optimized C++ performance-productivity gap register-transfer-level modeling unified framework unique languages vertically integrated computer architecture research vertically integrated research methodology Computational modeling Computer architecture Computers Hardware Hardware design languages Object oriented modeling Productivity

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Development of Software Simulator of UAV Flight for the Studies of Trajectory Motion Systems 6

Development of Software Simulator of UAV Flight for the Stud...

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IEEE 6th Forum on Strategic Partnership of Universities and Enterprises of Hi-Tech Branches (Science. Education. Innovations) (SPUE)

作者： Filatov, Denis, V Devyatkin, Aleksey, V Polyakhov, N. D. St Petersburg Electrotech Univ LETI Dept Automat Control Syst St Petersburg Russia

ISBN: (纸本)9781538625712

The development of unmanned aerial vehicle's (UAV) trajectory control systems is an example from a whole class of tasks that are most efficiently solved by means of computer simulation. Flight simulation allows to safely debug and study different control algorithms along with providing an opportunity to train control systems based on neural networks. There is a large number of program simulators that allow simulating a flight with a high degree of physical processes' detailization, however, the simulation speed drops significantly due to high-precision calculations. The paper considers the development of a flight simulator intended for training neural network used in flight trajectory control systems. Key features of the designed simulator are high operation speed while retaining basic features of UAV dynamics, features of distance sensors taking into obstacles in the environment.

关键词： python programming language simulator UAV

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WINAlarm - innovative IoT-based information System as python service 28

WINAlarm - innovative IoT-based information System as Python...

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28th International Business-Information-Management-Association Conference

作者： Vasilescu, Anca Carstea, Claudia Pacurar, Cristina Maria Transilvania Univ Brasov Dept Math & Comp Sci Brasov Romania

ISBN: (纸本)9780986041983

The common features between an IoT-based system and an Information System are considered here together with the motivation of these similarities. The Internet of Things is an increasing significant area of research and having connections with other research fields like IS could be a helpful target for progress. Merging the systems technological properties with the hardware devices innovative support leads to the applications' opportunity of providing innovative homes, ready to improve the citizens' quality of life and to increase the human condition. In this context, an alarm innovative system is described here and the specific support of python programming language is highlighted. There is outstanding research contribution here consisting of applying specific methods and techniques for logical and physical design of a new product based on embedding innovation into systems.

关键词： computer-based information system intetnet of things python programming language innovative home

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python for Data Science For Dummies 1

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丛书名： For Dummies: Computers

2015年

作者： John Paul Mueller, Luca Massaron

ISBN: (数字)9781118844144

ISBN: (纸本)1118844181;9781118844182

Unleash the power of python for your data analysis projects with For Dummies! python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. python for Data Science For Dummies shows you how to take advantage of python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. Youll get familiar with the python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether youre new to data analysis or just new to python, python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.

关键词： data science data scientists data mining data analysis data warehousing python python for data analysis python programming python programming language python data numpy scipy beautifulsoup pandas matploblib data visualization data science books data science book data analysis books data analysis book data science guide data science reference data analysis guide data science jobs big data big data tools big data solutions big data management learn about big data books on big data books on big data management choosing big data solutions how to manage big data what is big data networking data storage cloud computing data analytics data management database management

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Effects of teaching a computer programming language via hybrid interface on anxiety, cognitive load level and achievement of high school students

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EDUCATION AND INFORMATION TECHNOLOGIES 2021年第5期26卷 5291-5309页

作者： Unal, Alper Topu, Fatma Burcu Minist Natl Educ Antalya Turkey Ataturk Univ KK Educ Fac Dept Comp Educ & Instruct Technol TR-25240 Erzurum Turkey

In this study, based on quasi-experimental research, was investigated the effects of teaching python programming language via Blockly tool, which had hybrid interface, on students' computer programming anxiety, cognitive load level, and achievement. Participants were 90 high school students, 44 of them in experimental group (hybrid interface) and 46 of them in control group (non-hybrid interface). According to results, there was a meaningful difference between programming achievement scores of students in favor of experimental group while there was no difference in terms of computer programming anxiety between groups. Moreover, after 10-week implementation process, students' anxiety increased in each group. It was found out cognitive load levels of both groups in the first week were higher than final week. Although both weekly and 10-week intrinsic, extraneous, germane, and total cognitive load levels of experimental group were lower than control group, there was no significantly difference between groups. Consequently, it can be said that programming via hybrid interface, using Blockly, has not an effect on students' computer programming anxiety positively whereas it helps to keep cognitive load at low level and to increase students' programming success more. It is recommended that considering these results to make computer programming education is more efficient in high schools and administrators encourage the teachers to use programming tool had hybrid interface such as Blockly.

关键词： programming via hybrid interface Blockly python programming language Computer programming anxiety Cognitive load High school students’ achievement

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