Weekly Writing Assignment, Week 6

TO: Prof. Ellis
FROM: Your Name
SUBJECT: Research Expedition Memo
DATE: 10/20/2021

[Part 1 text:] 250 words discussing your possible three terms and conclude with your final choice and why you chose it.

[Part 2 text:] The ten following sources form the beginning of my research on [state your selected topic again]. [Use the “Cite > APA” tool on each article page on Academic Search Complete (EBSCO) to find the citation and then copy-and-paste it below. And, remember to organize them alphabetically according to the first author’s last name as demonstrated below.]

Altintas, T., Gunes, A., & Sayan, H. (2016). A peer-assisted learning experience in computer programming language learning and developing computer programming skills. Innovations in Education & Teaching International, 53(3), 329–337. https://doi-org.citytech.ezproxy.cuny.edu/10.1080/14703297.2014.993418

Lagravière, J., Langguth, J., Prugger, M., Einkemmer, L., Ha, P. H., & Cai, X. (2019). Performance Optimization and Modeling of Fine-Grained Irregular Communication in UPC. Scientific Programming, 1–20. https://doi-org.citytech.ezproxy.cuny.edu/10.1155/2019/6825728

Logan, W. (2008). Is C Dead? EE: Evaluation Engineering, 47(5), 44–49. 

1 thought on “Weekly Writing Assignment, Week 6”

1. Josef Rodriguez
   Professor Ellis
   ENG 1133 OL96
   16 October 2021
   Research Expedition Memo
   TO: Prof. Ellis
   FROM: Josef Rodriguez
   SUBJECT: Research Expedition Memo
   DATE: 10/20/2021

   So the three possible topics that I’m going to be discussing will be computer engineering, Python programming language and Technical writing. First off, computer engineering is the branch of engineering that integrates several fields of computer science and electronic engineering that is required to aid in the development of computer hardware and software. The usual tasks that are involved in computer engineering are writing both software and firmware for embedded microcontrollers, learning how to design VLSI chips, designing analog sensors, designing mixed signal circuit boards, and designing operating systems. The second possible topic will be the python programming language. Python as we know it is an interpreted, object-oriented, high-level programming language with dynamic semantics. It has a unique design which helps put an emphasis on code readability with its use of significant indentation. Due to the fact that Python is an object-oriented programming language, this can help programmers aim to write clear, logical code for both small and large-scaled projects. Python consistently ranks as one of the most popular programming languages of all time. The third and final possible topic will be technical writing. Technical writing is the practice of documenting processes, such as software manuals or instructional materials. It was originally limited to user manuals of some sort but now includes reports, executive summary statements, and briefs. In other words, technical writing has come to encompass a whole lot more in terms of technical processes. The topic that I’m going to choose will be the Python programming language. I’m choosing the Python programming language as my topic because I enjoy it as a programming language and how efficient it truly is when it comes to coding websites and coding overall.

   Cisse, A., Peters, J., Lazzara, G., & Chiappisi, L. (2021). PyDSC: a simple tool to treat differential scanning calorimetry data. Journal of Thermal Analysis & Calorimetry, 145(2), 403–409. https://doi-org.citytech.ezproxy.cuny.edu/10.1007/s10973-020-09775-9
   Combrisson, E., Nest, T., Brovelli, A., Ince, R. A. A., Soto, J. L. P., Guillot, A., & Jerbi, K. (2020). Tensorpac: An open-source Python toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals. PLoS Computational Biology, 16(10), 1–18. https://doi-org.citytech.ezproxy.cuny.edu/10.1371/journal.pcbi.1008302
   Heybrock, S., Arnold, O., Gudich, I., Nixon, D., Vaytet, N., Herwig, K. W., & Iverson, E. B. (2020). Scipp: Scientific data handling with labeled multi-dimensional arrays for C++ and Python. Journal of Neutron Research, 22(2/3), 169–181. https://doi-org.citytech.ezproxy.cuny.edu/10.3233/JNR-190131

   Jiménez, A. C., Anzola, J. P., García-Díaz, V., González Crespo, R., & Zhao, L. (2020). PyDSLRep: A domain-specific language for robotic simulation in V-Rep. PLoS ONE, 15(7), 1–24. https://doi-org.citytech.ezproxy.cuny.edu/10.1371/journal.pone.0235271

   Lano, K., & Kolahdouz-Rahimi, S. (2021). Implementing QVT-R via semantic interpretation in UML-RSDS. Software & Systems Modeling, 20(3), 725–766. https://doi-org.citytech.ezproxy.cuny.edu/10.1007/s10270-020-00824-3

   Montemurro, M., Grassi, E., Pizzino, C. G., Bertotti, A., Ficarra, E., & Urgese, G. (2021). PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity. BMC Bioinformatics, 22(1), 1–21. https://doi-org.citytech.ezproxy.cuny.edu/10.1186/s12859-021-04277-3
   Okonkwo, C. W., & Ade-Ibijola, A. (2021). Python-Bot: A Chatbot for Teaching Python Programming. Engineering Letters, 29(1), 1–10.

   Riette, S. (2020). Development of “Physical Parametrizations with PYthon” (PPPY, version 1.1) and its usage to reduce the time-step dependency in a microphysical scheme. Geoscientific Model Development, 13(2), 443–460. https://doi-org.citytech.ezproxy.cuny.edu/10.5194/gmd-13-443-2020

   Rivas-Barragan, D., Mubeen, S., Guim Bernat, F., Hofmann-Apitius, M., & Domingo-Fernández, D. (2020). Drug2ways: Reasoning over causal paths in biological networks for drug discovery. PLoS Computational Biology, 16(12), 1–21. https://doi-org.citytech.ezproxy.cuny.edu/10.1371/journal.pcbi.1008464
   TRAWI, 323;SKI, PAWEŁ. (2020). Development of real gas model operating in gas turbine system in Python programming environment. Archives of Thermodynamics, 41(4), 23–61. https://doi-org.citytech.ezproxy.cuny.edu/10.24425/ather.2020.135853

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