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Studying Physics, Getting to Know python: RC Circuit, Simple Experiments, coding, and Data Analysis With Raspberry Pi

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COMPUTING IN SCIENCE & ENGINEERING 2021年第1期23卷 93-96页

作者： Mandanici, Andrea Sara, Salvatore Alessandro Fiumara, Giacomo Mandaglio, Giuseppe Univ Messina Dept MIFT I-98166 Messina Italy

Raspberry Pi (RPi) is a well-known single-board computer natively equipped with a Linux-based operating system, Raspbian, and a powerful programming language, python. In this article, we propose an integrated project on physics and computer science exploiting RPi and python: a set of lab activities, coding, and discussion related to the study of charging and discharging phases of a capacitor in an RC circuit. In our simple experiments, entirely computer-controlled, the RPi and python scripts are used to: (i) apply a known constant voltage to the circuit at a desired time;(ii) measure the voltage on selected circuit elements as a function of time;(iii) evaluate and analyze experimental data. This approach is based on inexpensive hardware and open source software. It allows a hands-on experience with electric circuits and with dedicated examples of python coding. The codes involve python modules such as Numpy, Scipy, and Matplotlib that prove to be easy to use and efficient for our goals, supporting the choice of python language for further study or research tasks.

关键词： Cameras Data Acquisition Data Analysis Image Colour Analysis Image Enhancement Image Restoration Linux Microcomputers Programming Languages Public Domain Software RC Circuits RC Circuit Simple Experiments Data Analysis Raspberry Pi R Pi Single Board Computer Linux Based Powerful Programming Language Integrated Project Computer Science Lab Activities Charging Discharging Phases Computer Controlled python Scripts Known Constant Voltage Desired Time Selected Circuit Elements Hands On Experience Electric Circuits python coding python Modules python Language Encoding Time Measurement Task Analysis python Operating Systems Open Source Software Computer Languages Voltage Measurement Circuit Analysis

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A Localized Gravity Modeling of the Upper Crust Beneath Central Zagros

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PURE AND APPLIED GEOPHYSICS 2022年第6-7期179卷 2365-2381页

作者： Ardestani, Vahid E. Fournier, Dominique Oldenburg, Douglas W. Univ Tehran Inst Geophys Tehran Iran Ctr Excellence Survey Engn & Disaster Management Tehran Iran Univ British Columbia UBC Earth & Ocean Sci Dept EOAS Vancouver BC Canada

The gravity data in a vast square including different formations of the Zagros mountains and central Iran block are investigated for delineating and modeling the localized geological strata and structures in the upper crust. The measured gravity data are firstly corrected for drift and free-air effects. The digital terrain model (DTM) is obtained through SRTM TIFF pictures with 1-arc-second resolution accessible for the survey area and its surroundings. A new approach is applied for Bouguer slab and terrain corrections. The Bouguer slab and the terrain corrections are considered as the gravity effect of the mass between the reference and the ground surfaces. The gravity effect of this mass is computed by its parameterization through advanced meshing algorithms (octree and quadtree) and then subtracted from the observed gravity data to obtain Bouguer gravity anomalies. De-trending is carried out via the polynomial fitting method. Upward continuation and radial power spectrum methods are utilized to obtain rough estimates from the depth of the anomalies. The residual gravity anomalies are inverted through a well-known algorithm used in an open-source script in python to obtain the subsurface density distribution. The inversion results are interpreted based on a geological point of view. The maximum depth of some regional anomalies provides valuable information about the unresolved maximum thickness of stratigraphic formations, especially the oldest strata (Precambrian and lower Paleozoic) that were exposed in some parts of the survey area and have a relatively higher density.

关键词： Gravity modeling upper crust Central Zagros inversion python coding

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Intelligent Program Correction and Evaluation System

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2024年第2期33卷 2350071-2350071页

作者： Ganguli, Isha Bhowmick, Rajat Subhra Biswas, Shivam Sil, Jaya Bennett Univ Sch Comp Sci Engn & Technol Greater Noida 201310 India Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur 711103 India

The growing popularity of computing applications has sparked the interest of students in computer programming languages. Minor mistakes are prevalent while writing small code blocks due to the coder's lack of knowledge and carelessness. Instead of merely providing syntax warnings, it would be better to offer developers with an Integrated Development Environment (IDE) that can automatically correct short code blocks containing mistakes. This makes code composition easier and more time-efficient, thus improving the efficacy of large-scale development environments. Because python is becoming more popular, the goal of our study is to enhance the efficiency of writing python code by offering an automatic code-correcting approach. Furthermore, automatic program evaluation has been performed to assist in the debugging of small code blocks, which will ultimately be employed in the creation of real-time computer applications. The proposed technique would be useful for new learners who wish to create small python code blocks for ease of writing and debugging on online platforms (like edX, Coursera, and Udacity). One of the major contributions of the project is to create an erroneous dataset of python coding that contains all potential forms of probable syntax errors. The dataset induces variety through the use of multiple coding templates and is used to train deep learning models. We used the state-of-the-art text-to-text T5 transformer network model to automatically repair and evaluate the incorrect code. The outcomes of auto-correction are examined using the ROUGE and BLEU scores, as well as accuracy. The model corrects python code with single, double, and the multiple number of errors with greater than 80% accuracy. Similarly, the performance of the basic T5 transformer network for program auto-evaluation with and without mistakes has been examined, and the model achieves greater than 65% accuracy in both cases. The proposed T5 base transformer outperforms the SOTA auto-co

关键词： python coding automatic correction of code automatic code evaluation T5 transformer network eLearning deep learning

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Neuromatch Academy: Teaching Computational Neuroscience with Global Accessibility

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TRENDS IN COGNITIVE SCIENCES 2021年第7期25卷 535-538页

作者： van Viegen, Tara Akrami, Athena Bonnen, Kathryn DeWitt, Eric Hyafil, Alexandre Ledmyr, Helena Lindsay, Grace W. Mineault, Patrick Murray, John D. Pitkow, Xaq Puce, Aina Sedigh-Sarvestani, Madineh Stringer, Carsen Achakulvisut, Titipat Alikarami, Elnaz Atay, Melvin Selim Batty, Eleanor Erlich, Jeffrey C. Galbraith, Byron V. Guo, Yueqi Juavinett, Ashley L. Krause, Matthew R. Li, Songting Pachitariu, Marius Straley, Elizabeth Valeriani, Davide Vaughan, Emma Vaziri-Pashkam, Maryam Waskom, Michael L. Blohm, Gunna Kording, Konra Schrater, Paul Wyble, Brad Escola, Sean Peters, Megan A. K. Princeton Univ Princeton Neurosci Inst Princeton NJ 08544 USA UCL Sainsbury Wellcome Ctr London England NYU Ctr Neural Sci New York NY 10003 USA Champalimaud Fdn Champalimaud Res Lisbon Portugal Univ Autonoma Barcelona Ctr Recerca Matemat Barcelona Spain Karolinska Inst Int Neuroinformat Coordinating Facil Stockholm Sweden UCL Gatsby Computat Neurosci Unit Sainsbury Wellcome Ctr London England Yale Univ Dept Psychiat New Haven CT 06520 USA Baylor Coll Med Dept Neurosci Houston TX 77030 USA Baylor Coll Med Ctr Neurosci & Artificial Intelligence Houston TX 77030 USA Rice Univ Dept Elect & Comp Engn POB 1892 Houston TX 77251 USA Indiana Univ Psychol & Brain Sci Bloomington IN USA Max Planck Florida Inst Neurosci Jupiter FL USA Howard Hughes Med Inst HHMI Janelia Res Campus Ashburn VA USA Univ Penn Dept Bioengn Philadelphia PA 19104 USA Univ Penn Dept Neurosci Philadelphia PA 19104 USA McGill Univ Fac Dent Montreal PQ Canada Middle East Tech Univ Ankara Turkey Harvard Univ Dept Neurol Cambridge MA 02138 USA New York Univ Shanghai Shanghai Peoples R China Talla Inc Boston MA USA Johns Hopkins Univ Dept Biomed Engn Baltimore MD USA Univ Calif San Diego Div Biol Sci La Jolla CA 92093 USA McGill Univ Montreal Neurol Inst Montreal PQ Canada Shanghai Jiao Tong Univ MOE LSC Sch Math Sci Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Nat Sci Shanghai Peoples R China Massachusetts Eye & Ear Dept Otolaryngol Head & Neck Surg Boston MA USA Harvard Med Sch Dept Otolaryngol Head & Neck Surg Boston MA 02115 USA Massachusetts Gen Hosp Dept Neurol Boston MA 02114 USA Simons Computat Neurosci Imbizo Int Brain Res Org IBRO Cape Town South Africa NIMH Lab Brain & Cognit Bethesda MD 20892 USA Queens Univ Ctr Neurosci Studies Kingston ON Canada Univ Minnesota Dept Psychol 75 E River Rd Minneapolis MN 55455 USA Univ Minnesota Dept Comp Sci & Engn Minneapolis MN USA Penn State Univ Dept Psychol Philadelphia PA USA Columbia Uni

Neuromatch Academy (NMA) designed and ran a fully online 3-week Computational Neuroscience Summer School for 1757 students with 191 teaching assistants (TAs) working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility.

关键词： computational neuroscience education accessibility inclusivity python coding data science

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Empirical Auto-Evaluation of python Code for Performance Analysis of Transformer Network Using T5 Architecture 8

Empirical Auto-Evaluation of Python Code for Performance Ana...

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8th International Conference on Smart Computing and Communications (ICSCC)

作者： Ganguli, Isha Bhowmick, Rajat Subhra Biswas, Shivam Sil, Jaya Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur India

ISBN: (纸本)9781728196879

The immense real-time applicability of python coding makes the task of evaluating the code highly intriguing, in the Natural Language Processing (NLP) domain. Evaluation of computer programs induces a challenge of logical and arithmetic understanding. Therefore, it is indeed very relevant to analyze the empirical ability of current state-of-the-art sequence-based neural architectures in evaluating small computer programs. One of the possible applications of such analysis is the auto-evaluation of erroneous python code. In this context, we focused our work on evaluating small python code blocks with or without error and examined the efficiency of the latest T5 Transformer network model in this task. In terms of accuracy, different Rouge scores, and BLEU scores, the performance measurements has been calculated. Observations reveal that T5 Transformer is able to compute the output for both correct and erroneous python code blocks with more than 65% accuracy.

关键词： python coding Syntax Error Deep learning T5 Transformer network Auto-Correction of code

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Multi-Objective Optimization of Residual Stress and Cost in Laser Shock Peening Process Using Finite Element Analysis and PSO Algorithm

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Lasers in Manufacturing and Materials Processing 2019年第4期6卷 398-423页

作者： Golabi, Sa’id Vakil, Mohammad Reza Amirsalari, Behzad Faculty of Mechanical Engineering University of Kashan Kashan Iran

Laser shock peening (LSP) is an effective process utilized for surface enhancement of metal parts so that generating compressive residual stresses (RS) on the surface improves fatigue life of the material. The main affecting parameters on surface negative residual stress are laser power, laser beam size and shape, peening pitch and pattern. Varying these parameters alters the magnitude and depth of RS as well as the cost of LSP. An integrated method for simulation of optimum LSP process is presented in this paper, in which Particle Swarm Optimization (PSO) technique was employed utilizing python coding in ABAQUS finite element environment to maximize the uniformity of compressive RS and minimize LSP cost on an Inconel 718 super-alloy specimen. The mentioned affecting parameters were selected as optimization parameters, and minimum acceptable amounts and depth of compressive RSs were two main design constraints. Simulation results were compared with previously published experimental ones, and optimum LSP variables were finally determined and presented for certain amount of design constraints. It was revealed that, relatively small circular laser beam, shot by square scanning pattern, leads to generate the most uniform RS with minimum LSP cost. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Finite element simulation Laser shock peening Particle swarm multi-objective optimization python coding Residual stress

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Raspberry Pi based Advanced Scheduled Home Automation System through E-mail 5

Raspberry Pi based Advanced Scheduled Home Automation System...

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5th IEEE International Conference on Computational Intelligence and Computing Research (IEEE ICCIC)

作者： Narender, M. Vijayalakshmi, M. Velammal Inst Technol Dept ECE Madras Tamil Nadu India

ISBN: (纸本)9781479939756

The process of automation is becoming popular because of its numerous applications. Home automation is something that deals with the control of domestic appliances with the help of local networking or by remote control. The main aim of this paper is to develop an advanced method of home automation with the application of Raspberry Pi (RPi) through reading the subject of the E-mail. An algorithm for the same has been developed using the python environment which is by default provided by Raspberry Pi. The automation process that is employed is a scheduled automation, which is done. The results show an efficiency of the algorithm employed to perform a scheduled automated. These instructions give you basic guidelines for preparing papers for conference proceedings.

关键词： Raspberry Pi python coding Ethernet Cable Video Cable Micro SD Card

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Fast permeability estimation using NMR well logging data log-normal decomposition

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 231卷

作者： Stefanelli, Denilson Santos, Lucio Tunes Vidal, Alexandre Campane Univ Campinas Unicamp Sch Mech Engn FEM Campinas SP Brazil Univ Campinas Unicamp Dept Appl Math Campinas SP Brazil Univ Campinas Unicamp Inst Geosci Campinas SP Brazil

Nuclear magnetic resonance (NMR) well logging and NMR core analysis provide a non-invasive approach to a fast reservoir rock pore structures evaluation. The NMR data is presented as a time distribution called transverse relaxation time spectrum (T2 spectrum) and, with proper adjustment, the area under the T2 distribution curve is directly related to the porosity of the reservoir rock. Based on the assumption that immovable and free fluid are present in pores of different sizes, NMR well logging and core analysis use these differences to characterize the fluids found inside the rock pores. In this way, a fixed T2 value can be selected as a cutoff. The amplitudes of the T2 spectrum that have their T2 coordinates below this fixed value are related to the fluids that are in the small pores (immovable fluids), and those that have their T2 coordinates above this value are related to the large pores (free or movable fluids). This T2 value is called T2cutof f and can be estimated in a laboratory with NMR data acquisition on water-saturated core samples. This is done by comparing the NMR acquisition data from a plug fully saturated with water with the NMR acquisition data taken from the same partially saturated plug. In this work, a new method to estimate the pore size distribution, a variable T2cutof f curve, and the Timur-Coates permeability curve of the formation along the reservoir rock is presented. The method is based on the decomposition in log-normal components of the T2 spectra of NMR well logging and on the permeability and porosity of plugs, measured in a laboratory.

关键词： Nuclear magnetic resonance Pore size estimation Permeability model Porosity Timur-Coates model Permeability prediction python coding

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Optimization of biogas yield from anaerobic co-digestion of corn-chaff and cow dung digestate: RSM and python approach

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HELIYON 2021年第11期7卷 e08255页

作者： Iweka, Sunday Chukwuka Owuama, K. C. Chukwuneke, J. L. Falowo, O. A. Delta State Univ Sci & Technol Dept Mech Engn Ozoro Nigeria Chukwuemeka Odumegwu Ojukwu Univ Dept Mech Engn Uli Nigeria Nnamdi Azikiwe Univ Dept Mech Engn Awka Nigeria Landmark Univ Dept Chem Engn Omu Aran Nigeria

The utilization of various feedstocks of unique characteristics in producing biogas could potentially enhance the application of clean fuel from biomass wastes. Two modelling tools were used to explore biogas production from plant and animal wastes. In this study, corn chaff was inoculated with cow dung digestate using different mixing ratios of substrate/inoculum (S/I) of 1:1, 1:1.55, and 1:3.5 for hydraulic retention time (HRT) of 25, 31, and 37 days as modelled using Central Composite Design (Face Centered Design) to optimize the process and predict the optimal response. The result shows that the mixture ratio of 1:1.55 for 37 days gave a cumulative highest biogas yield of 6.19 L under mesophilic conditions. The model p-value is <0.0001, an indication that the model term is significant. The python coding of the input factors gave the optimal value of 4.71 L, which is similar to the result obtained via CCD. Thus, both CCD (Face Centered Design) and python coding are reliable in the optimization of biogas production as they both predicted the same optimal values and approximately the same highest cumulative biogas yield. The GC-MS characterization of produced biogas revealed that it contains 68% methane and 22.76% CO2. Other constituents present are confirmed by FTIR analysis results. The methane in produced biogas has a flashpoint of -182 degrees C, which is extremely flammable. This data shows that both CCD and python coding can model biogas production with high accuracy and biogas produced can be used for heating purposes.

关键词： Cow dung digestate Anaerobic digestion Corn chaff Biogas production Optimization python coding RSM

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Beginning programming with python for dummies 1

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丛书名： For dummies

2014年

作者： John Paul Mueller

ISBN: (数字)9781118891490

ISBN: (纸本)1118891457;9781118891452;9781118891476;9781118891490

Learn python--the fun and easy way--and get in the programming game today! python is one of the fastest growing programming languages, and no wonder. It requires three to five times less time than developing in Java, is a great building block for learning both procedural and object-oriented programming concepts, and is an ideal language for data analysis. Beginning Programming with python For Dummies is the perfect guide to this dynamic and powerful programming language--even if you've never coded before! Author John Paul Mueller draws on his vast programming knowledge and experience to guide you step-by-step through the syntax and logic of programming with python and provides several real-world programming examples to give you hands-on experience trying out what you've learned. Provides a solid understanding of basic computer programming concepts and helps familiarize you with syntax and logic Explains the fundamentals of procedural and object-oriented programming Shows how python is being used for data analysis and other applications Includes short, practical programming samples to apply your skills to real-world programming scenarios Whether you've never written a line of code or are just trying to pick up python, there's nothing to fear with the fun and friendly Beginning Programming with python For Dummies leading the way.

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